lint

extensions/ngrok/script.py

@@ -22,6 +22,7 @@ options = {
     'session_metadata': 'text-generation-webui',
 }
 
+
 def ui():
     settings = shared.settings.get("ngrok")
     if settings:
@@ -33,4 +34,3 @@ def ui():
         logging.info(f"Ingress established at: {tunnel.url()}")
     except ModuleNotFoundError:
         logging.error("===> ngrok library not found, please run `pip install -r extensions/ngrok/requirements.txt`")
-

extensions/openai/completions.py

@@ -380,7 +380,6 @@ def stream_chat_completions(body: dict, is_legacy: bool=False):
 
         yield chunk
 
-
     stop_reason = "stop"
     if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
         stop_reason = "length"
@@ -583,7 +582,6 @@ def stream_completions(body: dict, is_legacy: bool=False):
         completion_token_count += len(encode(new_content)[0])
         yield chunk
 
-
     stop_reason = "stop"
     if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
         stop_reason = "length"

extensions/openai/defaults.py

@@ -41,10 +41,13 @@ default_req_params = {
     # 'requested_model' - temporarily used
 }
 
+
 def get_default_req_params():
     return copy.deepcopy(default_req_params)
 
 # little helper to get defaults if arg is present but None and should be the same type as default.
+
+
 def default(dic, key, default):
     val = dic.get(key, default)
     if type(val) != type(default):
@@ -59,6 +62,6 @@ def default(dic, key, default):
         val = default
     return val
 
+
 def clamp(value, minvalue, maxvalue):
     return max(minvalue, min(value, maxvalue))
-

extensions/openai/embeddings.py

@@ -6,6 +6,7 @@ from extensions.openai.errors import *
 st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
 embeddings_model = None
 
+
 def load_embedding_model(model):
     try:
         emb_model = SentenceTransformer(model)
@@ -16,16 +17,19 @@ def load_embedding_model(model):
 
     return emb_model
 
+
 def get_embeddings_model():
     global embeddings_model, st_model
     if st_model and not embeddings_model:
         embeddings_model = load_embedding_model(st_model)  # lazy load the model
     return embeddings_model
 
+
 def get_embeddings_model_name():
     global st_model
     return st_model
 
+
 def embeddings(input: list, encoding_format: str):
 
     embeddings = get_embeddings_model().encode(input).tolist()

extensions/openai/errors.py

@@ -3,6 +3,7 @@ class OpenAIError(Exception):
         self.message = message
         self.code = code
         self.internal_message = internal_message
+
     def __repr__(self):
         return "%s(message=%r, code=%d)" % (
             self.__class__.__name__,
@@ -10,10 +11,12 @@ class OpenAIError(Exception):
             self.code,
         )
 
+
 class InvalidRequestError(OpenAIError):
     def __init__(self, message, param, code=400, error_type='InvalidRequestError', internal_message=''):
         super(OpenAIError, self).__init__(message, code, error_type, internal_message)
         self.param = param
+
     def __repr__(self):
         return "%s(message=%r, code=%d, param=%s)" % (
             self.__class__.__name__,
@@ -22,6 +25,7 @@ class InvalidRequestError(OpenAIError):
             self.param,
         )
 
+
 class ServiceUnavailableError(OpenAIError):
     def __init__(self, message=None, code=500, error_type='ServiceUnavailableError', internal_message=''):
         super(OpenAIError, self).__init__(message, code, error_type, internal_message)

extensions/openai/images.py

@@ -3,6 +3,7 @@ import time
 import requests
 from extensions.openai.errors import *
 
+
 def generations(prompt: str, size: str, response_format: str, n: int):
     # Stable Diffusion callout wrapper for txt2img
     # Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E

extensions/openai/models.py

@@ -7,15 +7,18 @@ from modules.models_settings import (get_model_settings_from_yamls,
 from extensions.openai.embeddings import get_embeddings_model_name
 from extensions.openai.errors import *
 
+
 def get_current_model_list() -> list:
     return [shared.model_name]  # The real chat/completions model, maybe "None"
 
+
 def get_pseudo_model_list() -> list:
     return [  # these are expected by so much, so include some here as a dummy
         'gpt-3.5-turbo',
         'text-embedding-ada-002',
     ]
 
+
 def load_model(model_name: str) -> dict:
     resp = {
         "id": model_name,
@@ -74,4 +77,3 @@ def model_info(model_name: str) -> dict:
         "owned_by": "user",
         "permission": []
     }
-

extensions/openai/moderations.py

@@ -48,7 +48,6 @@ def moderations(input):
 
     category_embeddings = get_category_embeddings()
 
-
     # input, string or array
     if isinstance(input, str):
         input = [input]

extensions/openai/script.py

@@ -22,6 +22,7 @@ params = {
     'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001,
 }
 
+
 class Handler(BaseHTTPRequestHandler):
     def send_access_control_headers(self):
         self.send_header("Access-Control-Allow-Origin", "*")

extensions/openai/tokens.py

@@ -1,6 +1,7 @@
 from extensions.openai.utils import float_list_to_base64
 from modules.text_generation import encode, decode
 
+
 def token_count(prompt):
     tokens = encode(prompt)[0]
 

extensions/openai/utils.py

@@ -2,6 +2,7 @@ import os
 import base64
 import numpy as np
 
+
 def float_list_to_base64(float_list):
     # Convert the list to a float32 array that the OpenAPI client expects
     float_array = np.array(float_list, dtype="float32")
@@ -16,11 +17,13 @@ def float_list_to_base64(float_list):
     ascii_string = encoded_bytes.decode('ascii')
     return ascii_string
 
+
 def end_line(s):
     if s and s[-1] != '\n':
         s = s + '\n'
     return s
 
+
 def debug_msg(*args, **kwargs):
     if 'OPENEDAI_DEBUG' in os.environ:
         print(*args, **kwargs)

extensions/sd_api_pictures/script.py

@@ -126,6 +126,8 @@ def input_modifier(string):
     return string
 
 # Get and save the Stable Diffusion-generated picture
+
+
 def get_SD_pictures(description, character):
 
     global params
@@ -186,6 +188,8 @@ def get_SD_pictures(description, character):
 
 # TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
 # and replace it with 'text' for the purposes of logging?
+
+
 def output_modifier(string, state):
     """
     This function is applied to the model outputs.

modules/sampler_hijack.py

@@ -126,6 +126,7 @@ class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
     '''
     Copied from the transformers library
     '''
+
     def __init__(self, penalty: float, _range: int):
         if not isinstance(penalty, float) or not (penalty > 0):
             raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")