clean up

playground/agents/meta_prompter.py

@@ -2,23 +2,23 @@
 from swarms.agents.meta_prompter import MetaPrompterAgent
 from langchain.llms import OpenAI
 
-#init llm
+# init llm
 llm = OpenAI()
 
-#init the meta prompter agent that optimized prompts
+# init the meta prompter agent that optimized prompts
 meta_optimizer = MetaPrompterAgent(llm=llm)
 
-#init the worker agent
+# init the worker agent
 worker = Worker(llm)
 
-#broad task to complete
+# broad task to complete
 task = "Create a feedforward in pytorch"
 
-#optimize the prompt
-optimized_prompt = meta_optimizer.run(task)    
+# optimize the prompt
+optimized_prompt = meta_optimizer.run(task)
 
-#run the optimized prompt with detailed instructions
+# run the optimized prompt with detailed instructions
 result = worker.run(optimized_prompt)
 
-#print
-print(result)
+# print
+print(result)

swarms/agents/conversabe_agent.py

@@ -977,7 +977,7 @@ def execute_code_blocks(self, code_blocks):
                 )
             elif lang in ["python", "Python"]:
                 if code.startswith("# filename: "):
-                    filename = code[11: code.find("\n")].strip()
+                    filename = code[11 : code.find("\n")].strip()
                 else:
                     filename = None
                 exitcode, logs, image = self.run_code(

swarms/agents/message.py

@@ -23,6 +23,6 @@ def __init__(self, sender, content, metadata=None):
 
     def __repr__(self):
         """
-        __repr__ means... 
+        __repr__ means...
         """
         return f"{self.timestamp} - {self.sender}: {self.content}"

swarms/agents/models/groundingdino/datasets/transforms.py

@@ -38,7 +38,7 @@ def crop(image, target, region):
 
     if "masks" in target:
         # FIXME should we update the area here if there are no boxes?
-        target["masks"] = target["masks"][:, i: i + h, j: j + w]
+        target["masks"] = target["masks"][:, i : i + h, j : j + w]
         fields.append("masks")
 
     # remove elements for which the boxes or masks that have zero area

swarms/agents/models/groundingdino/models/GroundingDINO/backbone/backbone.py

@@ -159,7 +159,7 @@ def __init__(
         ), "Only resnet50 and resnet101 are available."
         assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
         num_channels_all = [256, 512, 1024, 2048]
-        num_channels = num_channels_all[4 - len(return_interm_indices):]
+        num_channels = num_channels_all[4 - len(return_interm_indices) :]
         super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
 
 
@@ -224,7 +224,7 @@ def build_backbone(args):
             use_checkpoint=use_checkpoint,
         )
 
-        bb_num_channels = backbone.num_features[4 - len(return_interm_indices):]
+        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
     else:
         raise NotImplementedError("Unknown backbone {}".format(args.backbone))
 

swarms/agents/models/groundingdino/models/GroundingDINO/backbone/swin_transformer.py

@@ -649,7 +649,7 @@ def __init__(
                 qk_scale=qk_scale,
                 drop=drop_rate,
                 attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]): sum(depths[: i_layer + 1])],
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
                 norm_layer=norm_layer,
                 # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
                 downsample=downsamplelist[i_layer],

swarms/agents/models/groundingdino/models/GroundingDINO/bertwarper.py

@@ -221,9 +221,9 @@ def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer
             position_ids[row, col] = 0
         else:
             attention_mask[
-                row, previous_col + 1: col + 1, previous_col + 1: col + 1
+                row, previous_col + 1 : col + 1, previous_col + 1 : col + 1
             ] = True
-            position_ids[row, previous_col + 1: col + 1] = torch.arange(
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
                 0, col - previous_col, device=input_ids.device
             )
 
@@ -273,13 +273,13 @@ def generate_masks_with_special_tokens_and_transfer_map(
             position_ids[row, col] = 0
         else:
             attention_mask[
-                row, previous_col + 1: col + 1, previous_col + 1: col + 1
+                row, previous_col + 1 : col + 1, previous_col + 1 : col + 1
             ] = True
-            position_ids[row, previous_col + 1: col + 1] = torch.arange(
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
                 0, col - previous_col, device=input_ids.device
             )
             c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
-            c2t_maski[previous_col + 1: col] = True
+            c2t_maski[previous_col + 1 : col] = True
             cate_to_token_mask_list[row].append(c2t_maski)
         previous_col = col
 

swarms/agents/models/groundingdino/models/GroundingDINO/utils.py

@@ -76,7 +76,7 @@ def gen_encoder_output_proposals(
     proposals = []
     _cur = 0
     for lvl, (H_, W_) in enumerate(spatial_shapes):
-        mask_flatten_ = memory_padding_mask[:, _cur: (_cur + H_ * W_)].view(
+        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(
             N_, H_, W_, 1
         )
         valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)

swarms/agents/models/groundingdino/util/utils.py

@@ -619,7 +619,7 @@ def get_phrases_from_posmap(
 ):
     assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
     if posmap.dim() == 1:
-        posmap[0: left_idx + 1] = False
+        posmap[0 : left_idx + 1] = False
         posmap[right_idx:] = False
         non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
         token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]

swarms/agents/models/groundingdino/util/vl_utils.py

@@ -41,7 +41,7 @@ def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
                 positive_map[j, beg_pos] = 1
                 break
             else:
-                positive_map[j, beg_pos: end_pos + 1].fill_(1)
+                positive_map[j, beg_pos : end_pos + 1].fill_(1)
 
     return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
 

swarms/agents/models/segment_anything/segment_anything/modeling/mask_decoder.py

@@ -139,7 +139,7 @@ def predict_masks(
         # Run the transformer
         hs, src = self.transformer(src, pos_src, tokens)
         iou_token_out = hs[:, 0, :]
-        mask_tokens_out = hs[:, 1: (1 + self.num_mask_tokens), :]
+        mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :]
 
         # Upscale mask embeddings and predict masks using the mask tokens
         src = src.transpose(1, 2).view(b, c, h, w)

swarms/agents/models/segment_anything/segment_anything/utils/amg.py

@@ -101,7 +101,7 @@ def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]:
     ), "Batched iteration must have inputs of all the same size."
     n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0)
     for b in range(n_batches):
-        yield [arg[b * batch_size: (b + 1) * batch_size] for arg in args]
+        yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args]
 
 
 def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]:
@@ -142,7 +142,7 @@ def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray:
     idx = 0
     parity = False
     for count in rle["counts"]:
-        mask[idx: idx + count] = parity
+        mask[idx : idx + count] = parity
         idx += count
         parity ^= True
     mask = mask.reshape(w, h)

swarms/agents/multi_modal_visual_agent.py

@@ -207,12 +207,12 @@ def blend_gt2pt(old_image, new_image, sigma=0.15, steps=100):
     kernel[steps:-steps, :steps] = left
     kernel[steps:-steps, -steps:] = right
 
-    pt_gt_img = easy_img[pos_h: pos_h + old_size[1], pos_w: pos_w + old_size[0]]
+    pt_gt_img = easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]]
     gaussian_gt_img = (
         kernel * gt_img_array + (1 - kernel) * pt_gt_img
     )  # gt img with blur img
     gaussian_gt_img = gaussian_gt_img.astype(np.int64)
-    easy_img[pos_h: pos_h + old_size[1], pos_w: pos_w + old_size[0]] = gaussian_gt_img
+    easy_img[pos_h : pos_h + old_size[1], pos_w : pos_w + old_size[0]] = gaussian_gt_img
     gaussian_img = Image.fromarray(easy_img)
     return gaussian_img
 

swarms/agents/multi_modal_workers/omni_agent/omni_chat.py

@@ -317,7 +317,7 @@ def find_json(s):
     s = s.replace("'", '"')
     start = s.find("{")
     end = s.rfind("}")
-    res = s[start: end + 1]
+    res = s[start : end + 1]
     res = res.replace("\n", "")
     return res
 

swarms/embeddings/openai.py

@@ -347,7 +347,7 @@ def _get_len_safe_embeddings(
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j: j + self.embedding_ctx_length])
+                tokens.append(token[j : j + self.embedding_ctx_length])
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
@@ -366,7 +366,7 @@ def _get_len_safe_embeddings(
         for i in _iter:
             response = embed_with_retry(
                 self,
-                input=tokens[i: i + _chunk_size],
+                input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
             batched_embeddings.extend(r["embedding"] for r in response["data"])
@@ -428,15 +428,15 @@ async def _aget_len_safe_embeddings(
                 disallowed_special=self.disallowed_special,
             )
             for j in range(0, len(token), self.embedding_ctx_length):
-                tokens.append(token[j: j + self.embedding_ctx_length])
+                tokens.append(token[j : j + self.embedding_ctx_length])
                 indices.append(i)
 
         batched_embeddings: List[List[float]] = []
         _chunk_size = chunk_size or self.chunk_size
         for i in range(0, len(tokens), _chunk_size):
             response = await async_embed_with_retry(
                 self,
-                input=tokens[i: i + _chunk_size],
+                input=tokens[i : i + _chunk_size],
                 **self._invocation_params,
             )
             batched_embeddings.extend(r["embedding"] for r in response["data"])

swarms/models/chat_openai.py

@@ -458,7 +458,7 @@ def get_sub_prompts(
                 )
             params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
         sub_prompts = [
-            prompts[i: i + self.batch_size]
+            prompts[i : i + self.batch_size]
             for i in range(0, len(prompts), self.batch_size)
         ]
         return sub_prompts
@@ -469,7 +469,7 @@ def create_llm_result(
         """Create the LLMResult from the choices and prompts."""
         generations = []
         for i, _ in enumerate(prompts):
-            sub_choices = choices[i * self.n: (i + 1) * self.n]
+            sub_choices = choices[i * self.n : (i + 1) * self.n]
             generations.append(
                 [
                     Generation(

swarms/structs/link.py

@@ -50,21 +50,21 @@ def is_lc_serializable(self) -> bool:
 
     prompt: BasePromptTemplate
     """Prompt object to use."""
-    
+
     llm: BaseLanguageModel
     """Language model to call."""
-    
+
     output_key: str = "text"  #: :meta private:
-    
+
     output_parser: BaseLLMOutputParser = Field(default_factory=StrOutputParser)
     """Output parser to use.
     Defaults to one that takes the most likely string but does not change it 
     otherwise."""
-    
+
     return_final_only: bool = True
     """Whether to return only the final parsed result. Defaults to True.
     If false, will return a bunch of extra information about the generation."""
-    
+
     llm_kwargs: dict = Field(default_factory=dict)
 
     class Config:
@@ -349,4 +349,4 @@ def _link_type(self) -> str:
     def from_string(cls, llm: BaseLanguageModel, template: str) -> Link:
         """Create Link from LLM and template."""
         prompt_template = PromptTemplate.from_template(template)
-        return cls(llm=llm, prompt=prompt_template)
+        return cls(llm=llm, prompt=prompt_template)

swarms/structs/workflow.py

@@ -11,7 +11,7 @@ class Workflow:
     They string together multiple tasks of varying types, and can use Short-Term Memory
     or pass specific arguments downstream.
 
-    
+
     Usage
     llm = LLM()
     workflow = Workflow(llm)
@@ -47,11 +47,13 @@ def execute(self) -> Any:
             return response
 
     def __init__(self, agent, parallel: bool = False):
+        """__init__"""
         self.agent = agent
         self.tasks: List[Workflow.Task] = []
         self.parallel = parallel
 
     def add(self, task: str) -> Task:
+        """Add a task"""
         task = self.Task(task)
 
         if self.last_task():
@@ -62,12 +64,15 @@ def add(self, task: str) -> Task:
         return task
 
     def first_task(self) -> Optional[Task]:
+        """Add first task"""
         return self.tasks[0] if self.tasks else None
 
     def last_task(self) -> Optional[Task]:
+        """Last task"""
         return self.tasks[-1] if self.tasks else None
 
     def run(self, *args) -> Task:
+        """Run tasks"""
         [task.reset() for task in self.tasks]
 
         if self.parallel:
@@ -79,6 +84,7 @@ def run(self, *args) -> Task:
         return self.last_task()
 
     def context(self, task: Task) -> Dict[str, Any]:
+        """Context in tasks"""
         return {
             "parent_output": task.parents[0].output
             if task.parents and task.parents[0].output
@@ -88,6 +94,7 @@ def context(self, task: Task) -> Dict[str, Any]:
         }
 
     def __run_from_task(self, task: Optional[Task]) -> None:
+        """Run from task"""
         if task is None:
             return
         else:

swarms/tools/autogpt.py

@@ -1,29 +1,30 @@
-import interpreter
-from transformers import (
-    BlipForQuestionAnswering,
-    BlipProcessor,
-)
-from PIL import Image
-import torch
-from swarms.utils.logger import logger
-from pydantic import Field
-from langchain.tools.file_management.write import WriteFileTool
-from langchain.tools.file_management.read import ReadFileTool
-from langchain.tools import BaseTool
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.chains.qa_with_sources.loading import BaseCombineDocumentsChain
 import asyncio
 import os
-
-# Tools
 from contextlib import contextmanager
 from typing import Optional
 
+import interpreter
 import pandas as pd
+import torch
 from langchain.agents import tool
 from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
-from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
+from langchain.chains.qa_with_sources.loading import (
+    BaseCombineDocumentsChain,
+    load_qa_with_sources_chain,
+)
 from langchain.docstore.document import Document
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.tools import BaseTool
+from langchain.tools.file_management.read import ReadFileTool
+from langchain.tools.file_management.write import WriteFileTool
+from PIL import Image
+from pydantic import Field
+from transformers import (
+    BlipForQuestionAnswering,
+    BlipProcessor,
+)
+
+from swarms.utils.logger import logger
 
 ROOT_DIR = "./data/"
 
@@ -128,7 +129,7 @@ def _run(self, url: str, question: str) -> str:
         results = []
         # TODO: Handle this with a MapReduceChain
         for i in range(0, len(web_docs), 4):
-            input_docs = web_docs[i: i + 4]
+            input_docs = web_docs[i : i + 4]
             window_result = self.qa_chain(
                 {"input_documents": input_docs, "question": question},
                 return_only_outputs=True,