





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							:    Tuple          =logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE							:    Dict          ='''cuda''' if torch.cuda.is_available() else '''cpu'''


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__=100       ,lowerCAmelCase__=" " ):
				lowercase							=     text.split(lowercase__ )
				return [character.join(text[i : i + n] ).strip() for i in range(0       ,len(lowercase__ )       ,lowercase__ )]


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				lowercase					,    lowercase							=     [], []
				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__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__ ):
				lowercase							=     ctx_tokenizer(
				    documents["""title"""]       ,documents["""text"""]       ,truncation=lowercase__       ,padding="""longest"""       ,return_tensors="""pt""" )["""input_ids"""]
				lowercase							=     ctx_encoder(input_ids.to(device=lowercase__ )       ,return_dict=lowercase__ ).pooler_output
				return {"embeddings": embeddings.detach().cpu().numpy()}


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,):
				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
				lowercase							=     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
				lowercase							=     dataset.map(lowercase__       ,batched=lowercase__       ,num_proc=processing_args.num_proc )

				# And compute the embeddings
				lowercase							=     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=lowercase__ )
				lowercase							=     DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
				lowercase							=     Features(
				    {"""text""": Value("""string""" ), """title""": Value("""string""" ), """embeddings""": Sequence(Value("""float32""" ) )} )  # optional, save as float32 instead of float64 to save space
				lowercase							=     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
				lowercase							=     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
				lowercase							=     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
				lowercase							=     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_		:
			_A   :int  				=      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[Any]  				=      field(
			    default=__a      ,      metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}      ,      )
			_A   :Any  				=      field(
			    default='''facebook/rag-sequence-nq'''      ,      metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}      ,      )
			_A   :Union[str, Any]  				=      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   :Any  				=      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   :List[str]  				=      field(
			    default=16      ,      metadata={
			        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
			    }      ,      )

@dataclass
class     A_		:
			_A   :Optional[int]  				=      field(
			    default=768      ,      metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}      ,      )
			_A   :List[str]  				=      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)

					__SCREAMING_SNAKE_CASE							:    int          =HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
					__SCREAMING_SNAKE_CASE       ,   __SCREAMING_SNAKE_CASE       ,   __SCREAMING_SNAKE_CASE							:    Optional[Any]          =parser.parse_args_into_dataclasses()
					with TemporaryDirectory() as tmp_dir:
										__SCREAMING_SNAKE_CASE							:    List[str]          =rag_example_args.output_dir or tmp_dir
										main(rag_example_args, processing_args, index_hnsw_args)





'''simple docstring'''

import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


UpperCAmelCase					 =	get_tests_dir('''fixtures''')


class 	lowerCAmelCase  (   unittest.TestCase ):







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=mock.Mock()
            __lowercase      		=500
            __lowercase      		={}
            __lowercase      		=HTTPError
            __lowercase      		={}

            # Download this model to make sure it's in the cache.
            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
            # Under the mock environment we get a 500 error when trying to reach the model.
            with mock.patch('requests.Session.request'    ,   return_value=__lowercase   ) as mock_head:
                 __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2'   )
                 # This check we did call the fake head request
                 mock_head.assert_called()







       def       snake_case (				self      :			Dict   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(
                'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json'   )






@is_staging_test
class 	lowerCAmelCase  (   unittest.TestCase ):







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            __lowercase      		=TOKEN
            HfFolder.save_token(__lowercase   )







       @classmethod
       def       snake_case (				cls      :			str   ):







            """simple docstring"""







            try:
                 delete_repo(token=cls._token    ,   repo_id='test-feature-extractor'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='valid_org/test-feature-extractor-org'   )
            except HTTPError:
                 pass

            try:
                 delete_repo(token=cls._token    ,   repo_id='test-dynamic-feature-extractor'   )
            except HTTPError:
                 pass







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='test-feature-extractor'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(f'''{USER}/test-feature-extractor'''   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained(__lowercase   )
            feature_extractor.push_to_hub('valid_org/test-feature-extractor'    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )

            # Reset repo
            delete_repo(token=self._token    ,   repo_id='valid_org/test-feature-extractor'   )

            # Push to hub via save_pretrained
            with tempfile.TemporaryDirectory() as tmp_dir:
                 feature_extractor.save_pretrained(
                     __lowercase    ,   repo_id='valid_org/test-feature-extractor-org'    ,   push_to_hub=__lowercase    ,   use_auth_token=self._token   )

            __lowercase      		=WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org'   )
            for k, v in feature_extractor.__dict__.items():
                 self.assertEqual(__lowercase    ,   getattr(__lowercase    ,   __lowercase   )   )







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            CustomFeatureExtractor.register_for_auto_class()
            __lowercase      		=CustomFeatureExtractor.from_pretrained(__lowercase   )

            feature_extractor.push_to_hub('test-dynamic-feature-extractor'    ,   use_auth_token=self._token   )

            # This has added the proper auto_map field to the config
            self.assertDictEqual(
                feature_extractor.auto_map    ,   {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'}    ,   )

            __lowercase      		=AutoFeatureExtractor.from_pretrained(
                f'''{USER}/test-dynamic-feature-extractor'''    ,   trust_remote_code=__lowercase   )
            # Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
            self.assertEqual(new_feature_extractor.__class__.__name__    ,   'CustomFeatureExtractor'   )


