




'''simple docstring'''







import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


A									=logging.getLogger(__name__)
torch.set_grad_enabled(False)
A									='cuda' if torch.cuda.is_available() else 'cpu'





def 		snake_case_							(_a			:							str					,     _a			:							List[str]=1_0_0					,     _a			:							Tuple=" "  ):
    UpperCAmelCase     =	text.split(_a  )
    return [character.join(text[i : i + n]  ).strip() for i in range(0					,     len(_a  )					,     _a  )]





def 		snake_case_							(_a			:							dict  ):
    UpperCAmelCase ,  UpperCAmelCase     =	[], []
    for title, text in zip(documents['''title''']					,     documents['''text''']  ):
        if text is not None:
            for passage in split_text(_a  ):
                titles.append(title if title is not None else ''''''  )
                texts.append(_a  )
    return {"title": titles, "text": texts}





def 		snake_case_							(_a			:							dict					,     _a			:							DPRContextEncoder					,     _a			:							DPRContextEncoderTokenizerFast  ):
    UpperCAmelCase     =	ctx_tokenizer(
        documents['''title''']					,     documents['''text''']					,     truncation=_a					,     padding='''longest'''					,     return_tensors='''pt'''  )['''input_ids''']
    UpperCAmelCase     =	ctx_encoder(input_ids.to(device=_a  )					,     return_dict=_a  ).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}





def 		snake_case_							(_a			:							"RagExampleArguments"					,     _a			:							"ProcessingArguments"					,     _a			:							"IndexHnswArguments"					,     ):
    ######################################
    logger.info('''Step 1 - Create the dataset'''  )
    ######################################

    # The dataset needed for RAG must have three columns:
    # - title (string): title of the document
    # - text (string): text of a passage of the document
    # - embeddings (array of dimension d): DPR representation of the passage
    # Let's say you have documents in tab-separated csv files with columns "title" and "text"
    assert os.path.isfile(rag_example_args.csv_path  ), "Please provide a valid path to a csv file"

    # You can load a Dataset object this way
    UpperCAmelCase     =	load_dataset(
        '''csv'''					,     data_files=[rag_example_args.csv_path]					,     split='''train'''					,     delimiter='''\t'''					,     column_names=['''title''', '''text''']  )

    # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

    # Then split the documents into passages of 100 words
    UpperCAmelCase     =	dataset.map(_a					,     batched=_a					,     num_proc=processing_args.num_proc  )

    # And compute the embeddings
    UpperCAmelCase     =	DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  ).to(device=_a  )
    UpperCAmelCase     =	DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name  )
    UpperCAmelCase     =	Features(
        {'''text''': Value('''string'''  ), '''title''': Value('''string'''  ), '''embeddings''': Sequence(Value('''float32'''  )  )}  )  # optional, save as float32 instead of float64 to save space
    UpperCAmelCase     =	dataset.map(
        partial(_a					,     ctx_encoder=_a					,     ctx_tokenizer=_a  )					,     batched=_a					,     batch_size=processing_args.batch_size					,     features=_a					,     )

    # And finally save your dataset
    UpperCAmelCase     =	os.path.join(rag_example_args.output_dir					,     '''my_knowledge_dataset'''  )
    dataset.save_to_disk(_a  )
    # from datasets import load_from_disk
    # dataset = load_from_disk(passages_path)  # to reload the dataset

    ######################################
    logger.info('''Step 2 - Index the dataset'''  )
    ######################################

    # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
    UpperCAmelCase     =	faiss.IndexHNSWFlat(index_hnsw_args.d					,     index_hnsw_args.m					,     faiss.METRIC_INNER_PRODUCT  )
    dataset.add_faiss_index('''embeddings'''					,     custom_index=_a  )

    # And save the index
    UpperCAmelCase     =	os.path.join(rag_example_args.output_dir					,     '''my_knowledge_dataset_hnsw_index.faiss'''  )
    dataset.get_index('''embeddings'''  ).save(_a  )
    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index


@dataclass
class     _a						:
       __a       :  str            =      field(
           default=str(Path(__a				).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""				)							,							metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}							,							)
       __a       :  Optional[str]            =      field(
           default=__a							,							metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}							,							)
       __a       :  str            =      field(
           default="""facebook/rag-sequence-nq"""							,							metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}							,							)
       __a       :  str            =      field(
           default="""facebook/dpr-ctx_encoder-multiset-base"""							,							metadata={
               """help""": (
                   """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
                   """ 'facebook/dpr-ctx_encoder-multiset-base'"""
               )
           }							,							)
       __a       :  Optional[str]            =      field(
           default=str(Path(__a				).parent / """test_run""" / """dummy-kb"""				)							,							metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}							,							)






@dataclass
class     _a						:
       __a       :  Optional[int]            =      field(
           default=__a							,							metadata={
               """help""": """The number of processes to use to split the documents into passages. Default is single process."""
           }							,							)
       __a       :  int            =      field(
           default=16							,							metadata={
               """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
           }							,							)






@dataclass
class     _a						:
       __a       :  int            =      field(
           default=768							,							metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}							,							)
       __a       :  int            =      field(
           default=128							,							metadata={
               """help""": (
                   """The number of bi-directional links created for every new element during the HNSW index construction."""
               )
           }							,							)


if __name__ == "__main__":
   logging.basicConfig(level=logging.WARNING)
   logger.setLevel(logging.INFO)

   A									=HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
   A					,   A					,   A									=parser.parse_args_into_dataclasses()
   with TemporaryDirectory() as tmp_dir:
      A									=rag_example_args.output_dir or tmp_dir
      main(rag_example_args, processing_args, index_hnsw_args)










'''simple docstring'''







import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 		snake_case_							(_a			:							List[str]  ):
    UpperCAmelCase     =	{}

    UpperCAmelCase     =	job['''started_at''']
    UpperCAmelCase     =	job['''completed_at''']

    UpperCAmelCase     =	date_parser.parse(_a  )
    UpperCAmelCase     =	date_parser.parse(_a  )

    UpperCAmelCase     =	round((end_datetime - start_datetime).total_seconds() / 60.0  )

    UpperCAmelCase     =	start
    UpperCAmelCase     =	end
    UpperCAmelCase     =	duration_in_min

    return job_info





def 		snake_case_							(_a			:							str					,     _a			:							List[str]=None  ):
    UpperCAmelCase     =	None
    if token is not None:
        UpperCAmelCase     =	{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

    UpperCAmelCase     =	F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
    UpperCAmelCase     =	requests.get(_a					,     headers=_a  ).json()
    UpperCAmelCase     =	{}

    try:
        job_time.update({job['''name''']: extract_time_from_single_job(_a  ) for job in result['''jobs''']}  )
        UpperCAmelCase     =	math.ceil((result['''total_count'''] - 1_0_0) / 1_0_0  )

        for i in range(_a  ):
            UpperCAmelCase     =	requests.get(url + F"&page={i + 2}"					,     headers=_a  ).json()
            job_time.update({job['''name''']: extract_time_from_single_job(_a  ) for job in result['''jobs''']}  )

        return job_time
    except Exception:
        print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"  )

    return {}


if __name__ == "__main__":
   A									=argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
   A									=parser.parse_args()

   A									=get_job_time(args.workflow_run_id)
   A									=dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

   for k, v in job_time.items():
      print(f"""{k}: {v["duration"]}""")




