


'''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_  :				List[str]						= logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_  :				Tuple						= """cuda""" if torch.cuda.is_available() else """cpu"""





def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						List[Any]=100					,				UpperCAmelCase_						:						str=" "		):
 lowerCamelCase_      =  text.split(UpperCAmelCase_		)
 return [character.join(text[i : i + n]		).strip() for i in range(0					,				len(UpperCAmelCase_		)					,				UpperCAmelCase_		)]





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





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





def __snake_case					(      UpperCAmelCase_						:						"RagExampleArguments"					,				UpperCAmelCase_						:						"ProcessingArguments"					,				UpperCAmelCase_						:						"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
 lowerCamelCase_      =  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
 lowerCamelCase_      =  dataset.map(UpperCAmelCase_					,				batched=UpperCAmelCase_					,				num_proc=processing_args.num_proc		)

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

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

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





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      default=str(Path(lowercase			).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=lowercase			,						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(lowercase			).parent / "test_run" / "dummy-kb"			)			,						metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}			,						)





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={
          "help": "The number of processes to use to split the documents into passages. Default is single process."
      }			,						)
  _lowerCamelCase		    =  field(
      default=16			,						metadata={
          "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
      }			,						)





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      default=7_68			,						metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}			,						)
  _lowerCamelCase		    =  field(
      default=1_28			,						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_  :				Dict						= HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
     a_	,   a_	,   a_  :				List[str]						= parser.parse_args_into_dataclasses()
     with TemporaryDirectory() as tmp_dir:
          a_  :				int						= rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)







'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy
     import tensorflow as tf

     from transformers.models.esm.modeling_tf_esm import (
         TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFEsmForMaskedLM,
         TFEsmForSequenceClassification,
         TFEsmForTokenClassification,
         TFEsmModel,
     )





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  13
   lowerCamelCase_      =  7
   lowerCamelCase_      =  True
   lowerCamelCase_      =  True
   lowerCamelCase_      =  True
   lowerCamelCase_      =  99
   lowerCamelCase_      =  32
   lowerCamelCase_      =  2
   lowerCamelCase_      =  4
   lowerCamelCase_      =  37
   lowerCamelCase_      =  "gelu"
   lowerCamelCase_      =  0.1
   lowerCamelCase_      =  0.1
   lowerCamelCase_      =  512
   lowerCamelCase_      =  16
   lowerCamelCase_      =  2
   lowerCamelCase_      =  0.02
   lowerCamelCase_      =  3
   lowerCamelCase_      =  4
   lowerCamelCase_      =  None






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size     )

   lowerCamelCase_      =  None
   if self.use_input_mask:
    lowerCamelCase_      =  random_attention_mask([self.batch_size, self.seq_length]     )

   lowerCamelCase_      =  None
   lowerCamelCase_      =  None
   lowerCamelCase_      =  None
   if self.use_labels:
    lowerCamelCase_      =  ids_tensor([self.batch_size]			,  self.type_sequence_label_size     )
    lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels     )
    lowerCamelCase_      =  ids_tensor([self.batch_size]			,  self.num_choices     )

   lowerCamelCase_      =  EsmConfig(
       vocab_size=self.vocab_size			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  pad_token_id=1			,  num_attention_heads=self.num_attention_heads			,  intermediate_size=self.intermediate_size			,  hidden_act=self.hidden_act			,  hidden_dropout_prob=self.hidden_dropout_prob			,  attention_probs_dropout_prob=self.attention_probs_dropout_prob			,  max_position_embeddings=self.max_position_embeddings			,  type_vocab_size=self.type_vocab_size			,  initializer_range=self.initializer_range			,  )

   return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






  def   snake_case		(    self     ):







   """simple docstring"""
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  self.prepare_config_and_inputs()

   lowerCamelCase_      =  True
   lowerCamelCase_      =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  vocab_size=2     )

   return (
       config,
       input_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModel(config=UpperCamelCase     )
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  [input_ids, input_mask]
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  model(UpperCamelCase     )

   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  True

   lowerCamelCase_      =  TFEsmModel(config=UpperCamelCase     )
   lowerCamelCase_      =  {
       "input_ids": input_ids,
       "attention_mask": input_mask,
       "encoder_hidden_states": encoder_hidden_states,
       "encoder_attention_mask": encoder_attention_mask,
   }
   lowerCamelCase_      =  model(UpperCamelCase     )

   lowerCamelCase_      =  [input_ids, input_mask]
   lowerCamelCase_      =  model(UpperCamelCase			,  encoder_hidden_states=UpperCamelCase     )

   # Also check the case where encoder outputs are not passed
   lowerCamelCase_      =  model(UpperCamelCase			,  attention_mask=UpperCamelCase     )

   self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmForMaskedLM(config=UpperCamelCase     )
   lowerCamelCase_      =  model([input_ids, input_mask]     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.num_labels
   lowerCamelCase_      =  TFEsmForTokenClassification(config=UpperCamelCase     )
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  config_and_inputs
   lowerCamelCase_      =  {"input_ids": input_ids, "attention_mask": input_mask}
   return config, inputs_dict





@require_tf
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (
      (
          TFEsmModel,
          TFEsmForMaskedLM,
          TFEsmForSequenceClassification,
          TFEsmForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase		    =  (
      {
          "feature-extraction": TFEsmModel,
          "fill-mask": TFEsmForMaskedLM,
          "text-classification": TFEsmForSequenceClassification,
          "token-classification": TFEsmForTokenClassification,
          "zero-shot": TFEsmForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*UpperCamelCase     )






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFEsmModel.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )






  @unittest.skip("Protein models do not support embedding resizing."     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass






  @unittest.skip("Protein models do not support embedding resizing."     )
  def   snake_case		(    self     ):







   """simple docstring"""
   pass







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    lowerCamelCase_      =  model_class(UpperCamelCase     )
    assert isinstance(model.get_input_embeddings()			,  tf.keras.layers.Layer     )
    if model_class is TFEsmForMaskedLM:
     # Output embedding test differs from the main test because they're a matrix, not a layer
     lowerCamelCase_      =  model.get_bias()
     assert isinstance(UpperCamelCase			,  UpperCamelCase     )
     for k, v in name.items():
      assert isinstance(UpperCamelCase			,  tf.Variable     )
    else:
     lowerCamelCase_      =  model.get_output_embeddings()
     assert x is None
     lowerCamelCase_      =  model.get_bias()
     assert name is None





@require_tf
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

   lowerCamelCase_      =  tf.constant([[0, 1, 2, 3, 4, 5]]     )
   lowerCamelCase_      =  model(UpperCamelCase     )[0]
   lowerCamelCase_      =  [1, 6, 33]
   self.assertEqual(list(output.numpy().shape     )			,  UpperCamelCase     )
   # compare the actual values for a slice.
   lowerCamelCase_      =  tf.constant(
       [
           [
               [8.921_518, -10.589_814, -6.4_671_307],
               [-6.3_967_156, -13.911_377, -1.1_211_915],
               [-7.781_247, -13.951_557, -3.740_592],
           ]
       ]     )
   self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,  expected_slice.numpy()			,  atol=1e-2     )     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"     )

   lowerCamelCase_      =  tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]     )
   lowerCamelCase_      =  model(UpperCamelCase     )[0]
   # compare the actual values for a slice.
   lowerCamelCase_      =  tf.constant(
       [
           [
               [0.14_443_092, 0.54_125_327, 0.3_247_739],
               [0.30_340_484, 0.00_526_676, 0.31_077_722],
               [0.32_278_043, -0.24_987_096, 0.3_414_628],
           ]
       ]     )
   self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,  expected_slice.numpy()			,  atol=1e-4     )     )



