






import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


__snake_case     :     Dict       =  namedtuple("""ExampleNamedTuple""", """a b c""")


class      __SCREAMING_SNAKE_CASE       ( unittest.TestCase):






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    torch.randn(5  ,    2 )
												lowerCAmelCase__         =    torch.device('cuda' ) if torch.cuda.is_available() else torch.device('cpu' )

												lowerCAmelCase__         =    send_to_device(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta.cpu()  ,    _UpperCamelCase ) )

												lowerCAmelCase__         =    send_to_device((tensor, [tensor, tensor], 1)  ,    _UpperCamelCase )
												self.assertIsInstance(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta[0].cpu()  ,    _UpperCamelCase ) )
												self.assertIsInstance(resulta[1]  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta[1][0].cpu()  ,    _UpperCamelCase ) )
												self.assertTrue(torch.equal(resulta[1][1].cpu()  ,    _UpperCamelCase ) )
												self.assertEqual(resulta[2]  ,    1 )

												lowerCAmelCase__         =    send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}  ,    _UpperCamelCase )
												self.assertIsInstance(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta['a'].cpu()  ,    _UpperCamelCase ) )
												self.assertIsInstance(resulta['b']  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta['b'][0].cpu()  ,    _UpperCamelCase ) )
												self.assertTrue(torch.equal(resulta['b'][1].cpu()  ,    _UpperCamelCase ) )
												self.assertEqual(resulta['c']  ,    1 )

												lowerCAmelCase__         =    send_to_device(ExampleNamedTuple(a=_UpperCamelCase  ,    b=[tensor, tensor]  ,    c=1 )  ,    _UpperCamelCase )
												self.assertIsInstance(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta.a.cpu()  ,    _UpperCamelCase ) )
												self.assertIsInstance(resulta.b  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta.b[0].cpu()  ,    _UpperCamelCase ) )
												self.assertTrue(torch.equal(resulta.b[1].cpu()  ,    _UpperCamelCase ) )
												self.assertEqual(resulta.c  ,    1 )

												lowerCAmelCase__         =    send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1} )  ,    _UpperCamelCase )
												self.assertIsInstance(_UpperCamelCase  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta['a'].cpu()  ,    _UpperCamelCase ) )
												self.assertIsInstance(resulta['b']  ,    _UpperCamelCase )
												self.assertTrue(torch.equal(resulta['b'][0].cpu()  ,    _UpperCamelCase ) )
												self.assertTrue(torch.equal(resulta['b'][1].cpu()  ,    _UpperCamelCase ) )
												self.assertEqual(resulta['c']  ,    1 )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												with self.assertRaises(_UpperCamelCase ) as cm:
																		lowerCAmelCase__         =    recursively_apply(torch.tensor  ,    (torch.tensor(1 ), 1)  ,    error_on_other_type=_UpperCamelCase )
												self.assertEqual(
												    str(cm.exception )  ,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'  ,    )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    torch.tensor([1, 2, 3, 4, 5] )
												self.assertEqual(listify(_UpperCamelCase )  ,    [1, 2, 3, 4, 5] )

												lowerCAmelCase__         =    torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )
												self.assertEqual(listify(_UpperCamelCase )  ,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )

												lowerCAmelCase__         =    torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )
												self.assertEqual(
												    listify(_UpperCamelCase )  ,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												with patch_environment(aa=1  ,    BB=2 ):
																		self.assertEqual(os.environ.get('AA' )  ,    '1' )
																		self.assertEqual(os.environ.get('BB' )  ,    '2' )

												self.assertNotIn('AA'  ,    os.environ )
												self.assertNotIn('BB'  ,    os.environ )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    RegressionModel()
												lowerCAmelCase__         =    model.forward
												lowerCAmelCase__         =    convert_outputs_to_fpaa(model.forward )
												lowerCAmelCase__         =    extract_model_from_parallel(_UpperCamelCase  ,    keep_fpaa_wrapper=_UpperCamelCase )
												lowerCAmelCase__         =    pickle.dumps(_UpperCamelCase )






						@require_cuda
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    RegressionModel()
												lowerCAmelCase__         =    model.forward
												lowerCAmelCase__         =    torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
												lowerCAmelCase__         =    convert_outputs_to_fpaa(model.forward )
												lowerCAmelCase__         =    extract_model_from_parallel(_UpperCamelCase  ,    keep_fpaa_wrapper=_UpperCamelCase )
												lowerCAmelCase__         =    pickle.dumps(_UpperCamelCase )






						@require_cuda
						@require_torch_min_version(version='2.0' )
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    RegressionModel()
												lowerCAmelCase__         =    model.forward
												lowerCAmelCase__         =    torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
												lowerCAmelCase__         =    convert_outputs_to_fpaa(model.forward )
												lowerCAmelCase__         =    torch.compile(model.forward  ,    backend='inductor' )
												lowerCAmelCase__         =    torch.randn(4  ,    10 ).cuda()
												lowerCAmelCase__         =    model(_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    RegressionModel()
												# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
												lowerCAmelCase__         =    torch.nn.parallel.DataParallel(_UpperCamelCase )
												lowerCAmelCase__         =    extract_model_from_parallel(_UpperCamelCase )

												self.assertEqual(_UpperCamelCase  ,    _UpperCamelCase )






						@require_torch_min_version(version='2.0' )
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    RegressionModel()
												lowerCAmelCase__         =    torch.compile(_UpperCamelCase )

												# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
												lowerCAmelCase__         =    torch.nn.parallel.DataParallel(_UpperCamelCase )
												lowerCAmelCase__         =    torch.compile(_UpperCamelCase )
												lowerCAmelCase__         =    extract_model_from_parallel(_UpperCamelCase )

												self.assertEqual(compiled_model._orig_mod  ,    compiled_model_unwrapped._orig_mod )







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3] )] )  ,    torch.device('cpu' ) )
												self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3] )} )  ,    torch.device('cpu' ) )
												self.assertIsNone(find_device([1, 'a'] ) )













import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]=100     ,							UpperCamelCase_							:       Optional[int]=" "		)	-> int:

						"""simple docstring"""



						lowerCAmelCase__         =    text.split(UpperCamelCase_		)
						return [character.join(text[i : i + n]		).strip() for i in range(0     ,							len(UpperCamelCase_		)     ,							UpperCamelCase_		)]







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



						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 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    load_dataset(
						    'csv'     ,							data_files=[csv_path]     ,							split='train'     ,							delimiter='\t'     ,							column_names=['title', 'text']		)
						lowerCAmelCase__         =    kb_dataset.map(
						    UpperCamelCase_     ,							batched=UpperCamelCase_     ,							num_proc=1		)  # if you want you can load already splitted csv.
						lowerCAmelCase__         =    [kb_dataset.shard(UpperCamelCase_     ,							UpperCamelCase_     ,							contiguous=UpperCamelCase_		) for i in range(UpperCamelCase_		)]
						lowerCAmelCase__         =    kb_list[process_num]

						lowerCAmelCase__         =    'data_' + str(UpperCamelCase_		)
						lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
						lowerCAmelCase__         =    ctx_encoder.to(device=UpperCamelCase_		)

						def embed(
						    UpperCamelCase_							:       dict     ,							UpperCamelCase_							:       DPRContextEncoder     ,							UpperCamelCase_							:       DPRContextEncoderTokenizerFast     ,							UpperCamelCase_							:       Tuple		) -> dict:
												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()}

						lowerCAmelCase__         =    Features(
						    {'text': Value('string'		), 'title': Value('string'		), 'embeddings': Sequence(Value('float32'		)		)}		)  # optional, save as float32 instead of float64 to save space

						lowerCAmelCase__         =    data_shrad.map(
						    partial(UpperCamelCase_     ,							ctx_encoder=UpperCamelCase_     ,							ctx_tokenizer=UpperCamelCase_     ,							device=UpperCamelCase_		)     ,							batched=UpperCamelCase_     ,							batch_size=16     ,							features=UpperCamelCase_     ,							)
						dataset.save_to_disk(UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []

						for shard_address in glob(str(UpperCamelCase_		) + '/*/'		):
												data_shard_list.append(load_from_disk(UpperCamelCase_		)		)

						lowerCAmelCase__         =    concatenate_datasets(UpperCamelCase_		)
						faiss.omp_set_num_threads(96		)

						lowerCAmelCase__         =    faiss.IndexHNSWFlat(768     ,							128     ,							faiss.METRIC_INNER_PRODUCT		)
						concat.add_faiss_index('embeddings'     ,							custom_index=UpperCamelCase_		)
						concat.get_index('embeddings'		).save(
						    UpperCamelCase_		)  # since we load the index in to memory,we can directly update the index in the disk





