'''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


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






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				List[Any]=1_0_0   ,    _lowercase    :				Optional[Any]=" "     )							->	List[str]:
  __UpperCAmelCase:					Optional[int]		= text.split(_lowercase     )
  return [character.join(text[i : i + n]     ).strip() for i in range(0   ,    len(_lowercase     )   ,    _lowercase     )]






def  UpperCamelCase__					(					_lowercase    :				dict     )							->	dict:
  __UpperCAmelCase, __UpperCAmelCase:					Dict		= [], []
  for title, text in zip(documents["""title"""]   ,    documents["""text"""]     ):
    if text is not None:
      for passage in split_text(_lowercase     ):
        titles.append(title if title is not None else """"""     )
        texts.append(_lowercase     )
  return {"title": titles, "text": texts}






def  UpperCamelCase__					(					_lowercase    :				dict   ,    _lowercase    :				DPRContextEncoder   ,    _lowercase    :				DPRContextEncoderTokenizerFast     )							->	dict:
  __UpperCAmelCase:					Optional[int]		= ctx_tokenizer(
      documents["""title"""]   ,    documents["""text"""]   ,    truncation=_lowercase   ,    padding="""longest"""   ,    return_tensors="""pt"""     )["""input_ids"""]
  __UpperCAmelCase:					Tuple		= ctx_encoder(input_ids.to(device=_lowercase     )   ,    return_dict=_lowercase     ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}






def  UpperCamelCase__					(					_lowercase    :				"RagExampleArguments"   ,    _lowercase    :				"ProcessingArguments"   ,    _lowercase    :				"IndexHnswArguments"   ,    )							->	Optional[int]:
  ######################################
  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:					int		= 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:					Tuple		= dataset.map(_lowercase   ,    batched=_lowercase   ,    num_proc=processing_args.num_proc     )

  # And compute the embeddings
  __UpperCAmelCase:					Tuple		= DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=_lowercase     )
  __UpperCAmelCase:					Union[str, Any]		= DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     )
  __UpperCAmelCase:					Tuple		= Features(
      {"""text""": Value("""string"""     ), """title""": Value("""string"""     ), """embeddings""": Sequence(Value("""float32"""     )     )}     )  # optional, save as float32 instead of float64 to save space
  __UpperCAmelCase:					Union[str, Any]		= dataset.map(
      partial(_lowercase   ,    ctx_encoder=_lowercase   ,    ctx_tokenizer=_lowercase     )   ,    batched=_lowercase   ,    batch_size=processing_args.batch_size   ,    features=_lowercase   ,    )

  # And finally save your dataset
  __UpperCAmelCase:					Dict		= os.path.join(rag_example_args.output_dir   ,    """my_knowledge_dataset"""     )
  dataset.save_to_disk(_lowercase     )
  # 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:					Any		= faiss.IndexHNSWFlat(index_hnsw_args.d   ,    index_hnsw_args.m   ,    faiss.METRIC_INNER_PRODUCT     )
  dataset.add_faiss_index("""embeddings"""   ,    custom_index=_lowercase     )

  # And save the index
  __UpperCAmelCase:					List[str]		= os.path.join(rag_example_args.output_dir   ,    """my_knowledge_dataset_hnsw_index.faiss"""     )
  dataset.get_index("""embeddings"""     ).save(_lowercase     )
  # dataset.load_faiss_index("embeddings", index_path)  # to reload the index




@dataclass
class    a :



  """simple docstring"""
  __lowerCAmelCase				      =					field(
      default=str(Path(__lowerCAmelCase				).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""				)						,  metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}						,  )
  __lowerCAmelCase				      =					field(
      default=__lowerCAmelCase						,  metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}						,  )
  __lowerCAmelCase				      =					field(
      default="""facebook/rag-sequence-nq"""						,  metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}						,  )
  __lowerCAmelCase				      =					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'"""
          )
      }						,  )
  __lowerCAmelCase				      =					field(
      default=str(Path(__lowerCAmelCase				).parent / """test_run""" / """dummy-kb"""				)						,  metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}						,  )




@dataclass
class    a :



  """simple docstring"""
  __lowerCAmelCase				      =					field(
      default=__lowerCAmelCase						,  metadata={
          """help""": """The number of processes to use to split the documents into passages. Default is single process."""
      }						,  )
  __lowerCAmelCase				      =					field(
      default=1_6						,  metadata={
          """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
      }						,  )




@dataclass
class    a :



  """simple docstring"""
  __lowerCAmelCase				      =					field(
      default=7_6_8						,  metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}						,  )
  __lowerCAmelCase				      =					field(
      default=1_2_8						,  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)

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






import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_	   =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_	   =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_	   =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_	   =			len(train_data)
SCREAMING_SNAKE_CASE_	   =			0.009






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]="train"     )							->	int:
  return calculate_hypothesis_value(_lowercase   ,    _lowercase     ) - output(
      _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					List[Any]		= 0
  for i in range(len(_lowercase     ) - 1     ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Optional[Any]:
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				str     )							->	Dict:
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]     )
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]     )
  return None






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Union[str, Any]=m     )							->	Optional[Any]:
  __UpperCAmelCase:					int		= 0
  for i in range(_lowercase     ):
    if index == -1:
      summation_value += _error(_lowercase     )
    else:
      summation_value += _error(_lowercase     ) * train_data[i][0][index]
  return summation_value






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Optional[int]:
  __UpperCAmelCase:					Any		= summation_of_cost_derivative(_lowercase   ,    _lowercase     ) / m
  return cost_derivative_value






def  UpperCamelCase__					(					)							->	Tuple:
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  __UpperCAmelCase:					str		= 0.00_00_02
  __UpperCAmelCase:					List[Any]		= 0
  __UpperCAmelCase:					Optional[Any]		= 0
  while True:
    j += 1
    __UpperCAmelCase:					Any		= [0, 0, 0, 0]
    for i in range(0   ,    len(_lowercase     )     ):
      __UpperCAmelCase:					str		= get_cost_derivative(i - 1     )
      __UpperCAmelCase:					List[str]		= (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        _lowercase   ,    _lowercase   ,    atol=_lowercase   ,    rtol=_lowercase   ,    ):
      break
    __UpperCAmelCase:					List[Any]		= temp_parameter_vector
  print(("""Number of iterations:""", j)     )






def  UpperCamelCase__					(					)							->	Dict:
  for i in range(len(_lowercase     )     ):
    print(("""Actual output value:""", output(_lowercase   ,    """test"""     ))     )
    print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase   ,    """test"""     ))     )


if __name__ == "__main__":
    run_gradient_descent()
    print('\nTesting gradient descent for a linear hypothesis function.\n')
    test_gradient_descent()