mahmoudajawad

def parse_multipart(rfile, boundary):
	boundary = b'--' + boundary
	rfile = re.compile(boundary+b'(?:\r\n|\n)').split(rfile.replace(b'\r\n'+boundary+b'--', b'').replace(b'\n'+boundary+b'--', b''))
	pattern = b'Content-Disposition: form-data; name="?([\$a-zA-Z0-9\.\-_\\\:]+)"?(?:; filename="?([a-zA-Z0-9\.\-_]+)"?(?:\r\n|\n)Content-Type: ([a-zA-Z0-9\.\-_]+\/[a-zA-Z0-9\.\-_]+)(?:\r\n|\n)|(?:\r\n|\n))(?:\r\n|\n)(.+)'
	multipart = {}
	for part in rfile:
		try:
			multipart_key = re.match(pattern, part, re.DOTALL).group(1)
			multipart[multipart_key] = [group for group in re.match(pattern, part, re.DOTALL).groups()]
			if multipart[multipart_key][3][-2] == 13 and multipart[multipart_key][3][-1] == 10:

mahmoudajawad

class ClassSingleton(type):
	def __new__(cls, cls_name, bases, attrs):
		for name, attr in attrs.items():
			if callable(attr):
				attrs[name] = classmethod(attr)
		return type.__new__(cls, cls_name, bases, attrs)
	
	def __init__(cls, cls_name, bases, attrs):
		cls.singleton()

mahmoudajawad

/*
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/

mahmoudajawad

/*
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/

mahmoudajawad

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM


model = "tiiuae/falcon-40b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model, padding_side="left")

pipeline = transformers.pipeline(