


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


__snake_case            =   logging.getLogger(__name__)
torch.set_grad_enabled(False)
__snake_case            =   "cuda" if torch.cuda.is_available() else "cpu"






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Tuple=100       ,  SCREAMING_SNAKE_CASE_   :		List[Any]=" "	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					ctx_tokenizer(
			    documents["""title"""]       ,  documents["""text"""]       ,  truncation=SCREAMING_SNAKE_CASE_       ,  padding="""longest"""       ,  return_tensors="""pt"""	)['input_ids']
			UpperCamelCase         =					ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE_	)       ,  return_dict=SCREAMING_SNAKE_CASE_	).pooler_output
			return {"embeddings": embeddings.detach().cpu().numpy()}






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		str       ,  ):





			"""simple docstring"""




			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(SCREAMING_SNAKE_CASE_       ,  batched=SCREAMING_SNAKE_CASE_       ,  num_proc=processing_args.num_proc	)

			# And compute the embeddings
			UpperCamelCase         =					DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=SCREAMING_SNAKE_CASE_	)
			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(SCREAMING_SNAKE_CASE_       ,  ctx_encoder=SCREAMING_SNAKE_CASE_       ,  ctx_tokenizer=SCREAMING_SNAKE_CASE_	)       ,  batched=SCREAMING_SNAKE_CASE_       ,  batch_size=processing_args.batch_size       ,  features=SCREAMING_SNAKE_CASE_       ,  )

			# And finally save your dataset
			UpperCamelCase         =					os.path.join(rag_example_args.output_dir       ,  """my_knowledge_dataset"""	)
			dataset.save_to_disk(SCREAMING_SNAKE_CASE_	)
			# 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=SCREAMING_SNAKE_CASE_	)

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




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




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




@dataclass
class    UpperCAmelCase					:
			lowercase						= field(
			    default=7_6_8      , metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}      , )
			lowercase						= 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)

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








import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import BertGenerationDecoder, BertGenerationEncoder




class    UpperCAmelCase					:




			def __init__(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		str=1_3       ,       __magic_name__							:		str=7       ,       __magic_name__							:		Optional[Any]=True       ,       __magic_name__							:		Optional[Any]=True       ,       __magic_name__							:		Tuple=9_9       ,       __magic_name__							:		List[str]=3_2       ,       __magic_name__							:		List[str]=5       ,       __magic_name__							:		int=4       ,       __magic_name__							:		Union[str, Any]=3_7       ,       __magic_name__							:		List[Any]="gelu"       ,       __magic_name__							:		Any=0.1       ,       __magic_name__							:		Union[str, Any]=0.1       ,       __magic_name__							:		List[Any]=5_0       ,       __magic_name__							:		Tuple=0.02       ,       __magic_name__							:		Optional[int]=True       ,       __magic_name__							:		Optional[Any]=None       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					parent
						UpperCamelCase         =					batch_size
						UpperCamelCase         =					seq_length
						UpperCamelCase         =					is_training
						UpperCamelCase         =					use_input_mask
						UpperCamelCase         =					vocab_size
						UpperCamelCase         =					hidden_size
						UpperCamelCase         =					num_hidden_layers
						UpperCamelCase         =					num_attention_heads
						UpperCamelCase         =					intermediate_size
						UpperCamelCase         =					hidden_act
						UpperCamelCase         =					hidden_dropout_prob
						UpperCamelCase         =					attention_probs_dropout_prob
						UpperCamelCase         =					max_position_embeddings
						UpperCamelCase         =					initializer_range
						UpperCamelCase         =					use_labels
						UpperCamelCase         =					scope




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size						)

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

						if self.use_labels:
									UpperCamelCase         =					ids_tensor([self.batch_size, self.seq_length]       ,       self.vocab_size						)

						UpperCamelCase         =					self.get_config()

						return config, input_ids, input_mask, token_labels




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						return BertGenerationConfig(
						    vocab_size=self.vocab_size       ,       hidden_size=self.hidden_size       ,       num_hidden_layers=self.num_hidden_layers       ,       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       ,       is_decoder=__magic_name__       ,       initializer_range=self.initializer_range       ,       )




			def   lowerCamelCase_					(						self							:		Union[str, Any]						):







						"""simple docstring"""







						(
						    (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  
						)         =					self.prepare_config_and_inputs()

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

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




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		Tuple       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]       ,       **__magic_name__							:		Optional[Any]       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					BertGenerationEncoder(config=__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__						)
						UpperCamelCase         =					model(__magic_name__						)
						self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		Dict       ,       __magic_name__							:		str       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		Tuple       ,       **__magic_name__							:		Union[str, Any]       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					True
						UpperCamelCase         =					BertGenerationEncoder(config=__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(
						    __magic_name__       ,       attention_mask=__magic_name__       ,       encoder_hidden_states=__magic_name__       ,       encoder_attention_mask=__magic_name__       ,       )
						UpperCamelCase         =					model(
						    __magic_name__       ,       attention_mask=__magic_name__       ,       encoder_hidden_states=__magic_name__       ,       )
						self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, self.seq_length, self.hidden_size)						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		Optional[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Optional[int]       ,       __magic_name__							:		str       ,       __magic_name__							:		Union[str, Any]       ,       **__magic_name__							:		Dict       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					True
						UpperCamelCase         =					True
						UpperCamelCase         =					BertGenerationDecoder(config=__magic_name__						).to(__magic_name__						).eval()

						# first forward pass
						UpperCamelCase         =					model(
						    __magic_name__       ,       attention_mask=__magic_name__       ,       encoder_hidden_states=__magic_name__       ,       encoder_attention_mask=__magic_name__       ,       use_cache=__magic_name__       ,       )
						UpperCamelCase         =					outputs.past_key_values

						# create hypothetical multiple next token and extent to next_input_ids
						UpperCamelCase         =					ids_tensor((self.batch_size, 3)       ,       config.vocab_size						)
						UpperCamelCase         =					ids_tensor((self.batch_size, 3)       ,       vocab_size=2						)

						# append to next input_ids and
						UpperCamelCase         =					torch.cat([input_ids, next_tokens]       ,       dim=-1						)
						UpperCamelCase         =					torch.cat([input_mask, next_mask]       ,       dim=-1						)

						UpperCamelCase         =					model(
						    __magic_name__       ,       attention_mask=__magic_name__       ,       encoder_hidden_states=__magic_name__       ,       encoder_attention_mask=__magic_name__       ,       output_hidden_states=__magic_name__       ,       )["""hidden_states"""][0]
						UpperCamelCase         =					model(
						    __magic_name__       ,       attention_mask=__magic_name__       ,       encoder_hidden_states=__magic_name__       ,       encoder_attention_mask=__magic_name__       ,       past_key_values=__magic_name__       ,       output_hidden_states=__magic_name__       ,       )["""hidden_states"""][0]

						# select random slice
						UpperCamelCase         =					ids_tensor((1,)       ,       output_from_past.shape[-1]						).item()
						UpperCamelCase         =					output_from_no_past[:, -3:, random_slice_idx].detach()
						UpperCamelCase         =					output_from_past[:, :, random_slice_idx].detach()

						self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

						# test that outputs are equal for slice
						self.parent.assertTrue(torch.allclose(__magic_name__       ,       __magic_name__       ,       atol=1e-3						)						)




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		Any       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       *__magic_name__							:		Optional[Any]       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					BertGenerationDecoder(__magic_name__						)
						model.to(__magic_name__						)
						model.eval()
						UpperCamelCase         =					model(__magic_name__       ,       attention_mask=__magic_name__       ,       labels=__magic_name__						)
						self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.seq_length, self.vocab_size)						)





			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					self.prepare_config_and_inputs()
						UpperCamelCase         =					{"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict




@require_torch
class    UpperCAmelCase					(       __snake_case      , __snake_case      , __snake_case      , unittest.TestCase    ):
			lowercase						= (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
			lowercase						= (BertGenerationDecoder,) if is_torch_available() else ()
			lowercase						= (
			    {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
			    if is_torch_available()
			    else {}
			)




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					BertGenerationEncoderTester(self						)
						UpperCamelCase         =					ConfigTester(self       ,       config_class=__magic_name__       ,       hidden_size=3_7						)




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						self.config_tester.run_common_tests()




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					self.model_tester.prepare_config_and_inputs()
						UpperCamelCase         =					"""bert"""
						self.model_tester.create_and_check_model(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__						)




			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_decoder_model_past_large_inputs(*__magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						(
						    (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  (
						    UpperCamelCase
						)  ,  
						)         =					self.model_tester.prepare_config_and_inputs_for_decoder()

						UpperCamelCase         =					None

						self.model_tester.create_and_check_model_as_decoder(
						    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       )




			def   lowerCamelCase_					(						self							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_for_causal_lm(*__magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		List[Any]						):







						"""simple docstring"""







						UpperCamelCase         =					BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""						)
						self.assertIsNotNone(__magic_name__						)




@require_torch
class    UpperCAmelCase					(       unittest.TestCase    ):




			@slow
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						UpperCamelCase         =					BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""						)
						UpperCamelCase         =					torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]						)
						with torch.no_grad():
									UpperCamelCase         =					model(__magic_name__						)[0]
						UpperCamelCase         =					torch.Size([1, 8, 1_0_2_4]						)
						self.assertEqual(output.shape       ,       __magic_name__						)
						UpperCamelCase         =					torch.tensor(
						    [[[0.1_775, 0.0_083, -0.0_321], [1.6_002, 0.1_287, 0.3_912], [2.1_473, 0.5_791, 0.6_066]]]						)
						self.assertTrue(torch.allclose(output[:, :3, :3]       ,       __magic_name__       ,       atol=1e-4						)						)




@require_torch
class    UpperCAmelCase					(       unittest.TestCase    ):




			@slow
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						UpperCamelCase         =					BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder"""						)
						UpperCamelCase         =					torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]						)
						with torch.no_grad():
									UpperCamelCase         =					model(__magic_name__						)[0]
						UpperCamelCase         =					torch.Size([1, 8, 5_0_3_5_8]						)
						self.assertEqual(output.shape       ,       __magic_name__						)
						UpperCamelCase         =					torch.tensor(
						    [[[-0.5_788, -2.5_994, -3.7_054], [0.0_438, 4.7_997, 1.8_795], [1.5_862, 6.6_409, 4.4_638]]]						)
						self.assertTrue(torch.allclose(output[:, :3, :3]       ,       __magic_name__       ,       atol=1e-4						)						)




