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| dfs_code = r""" | |
| def dfs(visited, graph, node): #function for dfs | |
| if node not in visited: | |
| print (node) | |
| visited.add(node) | |
| for neighbour in graph[node]: | |
| dfs(visited, graph, neighbour) | |
| """ | |
| function_code = r""" | |
| def write_documents(self, documents: Union[List[dict], List[Document]], index: Optional[str] = None, | |
| batch_size: int = 10_000, duplicate_documents: Optional[str] = None): | |
| if index and not self.client.indices.exists(index=index): | |
| self._create_document_index(index) | |
| if index is None: | |
| index = self.index | |
| duplicate_documents = duplicate_documents or self.duplicate_documents | |
| assert duplicate_documents in self.duplicate_documents_options, | |
| f"duplicate_documents parameter must be {', '.join(self.duplicate_documents_options)}" | |
| field_map = self._create_document_field_map() | |
| document_objects = [Document.from_dict(d, field_map=field_map) if isinstance(d, dict) else d for d in documents] | |
| document_objects = self._handle_duplicate_documents(documents=document_objects, | |
| index=index, | |
| duplicate_documents=duplicate_documents) | |
| documents_to_index = [] | |
| for doc in document_objects: | |
| _doc = { | |
| "_op_type": "index" if duplicate_documents == 'overwrite' else "create", | |
| "_index": index, | |
| **doc.to_dict(field_map=self._create_document_field_map()) | |
| } # type: Dict[str, Any] | |
| # cast embedding type as ES cannot deal with np.array | |
| if _doc[self.embedding_field] is not None: | |
| if type(_doc[self.embedding_field]) == np.ndarray: | |
| _doc[self.embedding_field] = _doc[self.embedding_field].tolist() | |
| # rename id for elastic | |
| _doc["_id"] = str(_doc.pop("id")) | |
| # don't index query score and empty fields | |
| _ = _doc.pop("score", None) | |
| _doc = {k:v for k,v in _doc.items() if v is not None} | |
| # In order to have a flat structure in elastic + similar behaviour to the other DocumentStores, | |
| # we "unnest" all value within "meta" | |
| if "meta" in _doc.keys(): | |
| for k, v in _doc["meta"].items(): | |
| _doc[k] = v | |
| _doc.pop("meta") | |
| documents_to_index.append(_doc) | |
| # Pass batch_size number of documents to bulk | |
| if len(documents_to_index) % batch_size == 0: | |
| bulk(self.client, documents_to_index, request_timeout=300, refresh=self.refresh_type) | |
| documents_to_index = [] | |
| if documents_to_index: | |
| bulk(self.client, documents_to_index, request_timeout=300, refresh=self.refresh_type) | |
| """ | |
| real_docstring = r""" | |
| Indexes documents for later queries in Elasticsearch. | |
| Behaviour if a document with the same ID already exists in ElasticSearch: | |
| a) (Default) Throw Elastic's standard error message for duplicate IDs. | |
| b) If `self.update_existing_documents=True` for DocumentStore: Overwrite existing documents. | |
| (This is only relevant if you pass your own ID when initializing a `Document`. | |
| If don't set custom IDs for your Documents or just pass a list of dictionaries here, | |
| they will automatically get UUIDs assigned. See the `Document` class for details) | |
| :param documents: a list of Python dictionaries or a list of Haystack Document objects. | |
| For documents as dictionaries, the format is {"content": "<the-actual-text>"}. | |
| Optionally: Include meta data via {"content": "<the-actual-text>", | |
| "meta":{"name": "<some-document-name>, "author": "somebody", ...}} | |
| It can be used for filtering and is accessible in the responses of the Finder. | |
| Advanced: If you are using your own Elasticsearch mapping, the key names in the dictionary | |
| should be changed to what you have set for self.content_field and self.name_field. | |
| :param index: Elasticsearch index where the documents should be indexed. If not supplied, self.index will be used. | |
| :param batch_size: Number of documents that are passed to Elasticsearch's bulk function at a time. | |
| :param duplicate_documents: Handle duplicates document based on parameter options. | |
| Parameter options : ( 'skip','overwrite','fail') | |
| skip: Ignore the duplicates documents | |
| overwrite: Update any existing documents with the same ID when adding documents. | |
| fail: an error is raised if the document ID of the document being added already | |
| exists. | |
| :raises DuplicateDocumentError: Exception trigger on duplicate document | |
| :return: None | |
| """ | |
| tree_code = r""" | |
| class Tree: | |
| def __init__(self): | |
| self.val = None | |
| self.left = None | |
| self.right = None | |
| """ | |
| insert_code = r""" | |
| def insert(self, val): | |
| if self.val: | |
| if val < self.val: | |
| if self.left is None: | |
| self.left = Tree(val) | |
| else: | |
| self.left.insert(val) | |
| elif val > self.val: | |
| if self.right is None: | |
| self.right = Tree(val) | |
| else: | |
| self.right.insert(val) | |
| else: | |
| self.val = val | |
| """ | |
| display_code = r""" | |
| def display_tree(self: Tree, prefix='value: '): | |
| current_node = self.val | |
| if self.left: | |
| self.left.display_tree() | |
| print(prefix, current_node) | |
| if self.right: | |
| self.right.display_tree() | |
| """ | |
| article_string = r"""CodeXGLLUE task definition (and dataset): **Code summarization (CodeSearchNet)**: | |
| _A model is given the task to generate natural language comments for a programming language code input._ | |
| For further details, see the [CodeXGLUE](https://github.com/microsoft/CodeXGLUE) benchmark dataset and open challenge for code intelligence. | |
| """ | |
| descr_string = 'The application takes as input the python code for a function, or a class, and generates a documentation string, or code comment, for it using codeT5 fine tuned for code2text generation. Code to text generation, or code summarization, is a CodeXGLUE generation, or sequence to sequence, downstream task. CodeXGLUE stands for General Language Understanding Evaluation benchmark *for code*, which includes diversified code intelligence downstream inference tasks and datasets.' | |
| def pygen_func(nl_code_intent): | |
| pass # TODO: generate code PL from intent NL + search in corpus | |
| # inputs = {'code_nl': code_nl} | |
| # payload = json.dumps(inputs) | |
| # prediction = req.request(CT5_METHOD, CT5_URL, data=payload) | |
| # prediction = req.request(CT5_METHOD, CT5_URL, json=req_data) | |
| # answer = json.loads(prediction.content.decode("utf-8")) | |
| # return str(answer) | |
| # CT5_URL = "https://api-inference.huggingface.co/models/nielsr/codet5-small-code-summarization-ruby" | |