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import asyncio
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import os, tempfile
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import logging
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import requests
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from transformers import AutoTokenizer, AutoConfig
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from huggingface_hub import hf_hub_download
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logger = logging.getLogger(__name__)
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async def generate_sync(
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prompt: str,
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api_key: str,
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bot_name: str,
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):
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# Set the API endpoint URL
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endpoint = "https://api.runpod.ai/v2/pygmalion-6b/runsync"
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# Set the headers for the request
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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max_new_tokens = 200
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prompt_num_tokens = await num_tokens(prompt)
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# Define your inputs
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input_data = {
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"input": {
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"prompt": prompt,
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"max_length": max(prompt_num_tokens+max_new_tokens, 2048),
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"temperature": 0.75,
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"do_sample": True,
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}
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}
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logger.info(f"sending request to runpod.io")
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# Make the request
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r = requests.post(endpoint, json=input_data, headers=headers, timeout=180)
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r_json = r.json()
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logger.info(r_json)
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status = r_json["status"]
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if status == 'COMPLETED':
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text = r_json["output"]
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answer = text.removeprefix(prompt)
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# lines = reply.split('\n')
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# reply = lines[0].strip()
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idx = answer.find(f"\nYou:")
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if idx != -1:
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reply = answer[:idx].strip()
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else:
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reply = answer.removesuffix('<|endoftext|>').strip()
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reply.replace("\n{bot_name}: ", " ")
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return reply
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elif status == 'IN_PROGRESS' or status == 'IN_QUEUE':
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job_id = r_json["id"]
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TIMEOUT = 180
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DELAY = 5
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for i in range(TIMEOUT//DELAY):
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endpoint = "https://api.runpod.ai/v2/pygmalion-6b/status/" + job_id
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r = requests.get(endpoint, headers=headers)
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r_json = r.json()
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logger.info(r_json)
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status = r_json["status"]
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if status == 'IN_PROGRESS':
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await asyncio.sleep(DELAY)
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elif status == 'IN_QUEUE':
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await asyncio.sleep(DELAY)
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elif status == 'COMPLETED':
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text = r_json["output"]
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answer = text.removeprefix(prompt)
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# lines = reply.split('\n')
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# reply = lines[0].strip()
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idx = answer.find(f"\nYou:")
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if idx != -1:
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reply = answer[:idx].strip()
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else:
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reply = answer.removesuffix('<|endoftext|>').strip()
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reply = reply.replace("\n{bot_name}: ", " ")
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return reply
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else:
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return "<ERROR>"
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else:
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return "<ERROR>"
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async def get_full_prompt(simple_prompt: str, bot, chat_history):
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# Prompt without history
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prompt = bot.name + "'s Persona: " + bot.persona + "\n"
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prompt += "Scenario: " + bot.scenario + "\n"
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prompt += "<START>" + "\n"
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#prompt += bot.name + ": " + bot.greeting + "\n"
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prompt += "You: " + simple_prompt + "\n"
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prompt += bot.name + ":"
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MAX_TOKENS = 2048
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max_new_tokens = 200
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total_num_tokens = await num_tokens(prompt)
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visible_history = []
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current_message = True
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for key, chat_item in reversed(chat_history.chat_history.items()):
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if current_message:
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current_message = False
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continue
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if chat_item.message["en"].startswith('!begin'):
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break
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if chat_item.message["en"].startswith('!'):
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continue
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#if chat_item.message["en"] == bot.greeting:
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# continue
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print("History: " + str(chat_item))
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if chat_item.num_tokens == None:
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chat_item.num_tokens = await num_tokens("{}: {}".format(chat_item.user_name, chat_item.message["en"]))
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# TODO: is it MAX_TOKENS or MAX_TOKENS - max_new_tokens??
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if total_num_tokens < (MAX_TOKENS - max_new_tokens):
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visible_history.append(chat_item)
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total_num_tokens += chat_item.num_tokens
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print(total_num_tokens)
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print("Finally: "+ str(total_num_tokens))
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visible_history = reversed(visible_history)
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prompt = bot.name + "'s Persona: " + bot.persona + "\n"
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prompt += "Scenario: " + bot.scenario + "\n"
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prompt += "<START>" + "\n"
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#prompt += bot.name + ": " + bot.greeting + "\n"
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for chat_item in visible_history:
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if chat_item.is_own_message:
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prompt += bot.name + ": " + chat_item.message["en"] + "\n"
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else:
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prompt += "You" + ": " + chat_item.message["en"] + "\n"
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prompt += "You: " + simple_prompt + "\n"
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prompt += bot.name + ":"
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return prompt
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async def num_tokens(input_text: str):
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# os.makedirs("./models/pygmalion-6b", exist_ok=True)
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# hf_hub_download(repo_id="PygmalionAI/pygmalion-6b", filename="config.json", cache_dir="./models/pygmalion-6b")
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# config = AutoConfig.from_pretrained("./models/pygmalion-6b/config.json")
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tokenizer = AutoTokenizer.from_pretrained("PygmalionAI/pygmalion-6b")
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encoding = tokenizer.encode(input_text, add_special_tokens=False)
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max_input_size = tokenizer.max_model_input_sizes
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return len(encoding)
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async def estimate_num_tokens(input_text: str):
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return len(input_text)//4+1
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async def generate_image(input_prompt: str, negative_prompt: str, api_key: str):
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# Set the API endpoint URL
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endpoint = "https://api.runpod.ai/v1/sd-anything-v4/run"
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# Set the headers for the request
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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# Define your inputs
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input_data = {
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"input": {
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"prompt": input_prompt,
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"negative_prompt": negative_prompt,
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"width": 512,
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"height": 768,
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"nsfw": True
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}
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}
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logger.info(f"sending request to runpod.io")
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# Make the request
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r = requests.post(endpoint, json=input_data, headers=headers)
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r_json = r.json()
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logger.info(r_json)
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if r.status_code == 200:
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status = r_json["status"]
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if status != 'IN_QUEUE':
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raise ValueError(f"RETURN CODE {status}")
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job_id = r_json["id"]
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TIMEOUT = 180
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DELAY = 5
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for i in range(TIMEOUT//DELAY):
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endpoint = "https://api.runpod.ai/v1/sd-anything-v4/status/" + job_id
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r = requests.get(endpoint, headers=headers)
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r_json = r.json()
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logger.info(r_json)
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status = r_json["status"]
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if status == 'IN_PROGRESS':
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await asyncio.sleep(DELAY)
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elif status == 'IN_QUEUE':
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await asyncio.sleep(DELAY)
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elif status == 'COMPLETED':
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output = r_json["output"]
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break
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else:
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raise ValueError(f"RETURN CODE {status}")
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os.makedirs("./images", exist_ok=True)
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files = []
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for image in output:
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temp_name = next(tempfile._get_candidate_names())
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filename = "./images/" + temp_name + ".jpg"
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await download_image(image["image"], filename)
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files.append(filename)
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return files
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async def download_image(url, path):
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r = requests.get(url, stream=True)
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if r.status_code == 200:
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with open(path, 'wb') as f:
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for chunk in r:
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f.write(chunk)
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