
"""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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


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






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






def        lowerCAmelCase_       (		snake_case_		:      dict      )       ->dict:
			lowerCamelCase__     :					Optional[Any]     	=[], []
			for title, text in zip(documents['title']							,   documents['text']      ):
						if text is not None:
									for passage in split_text(snake_case_      ):
												titles.append(title if title is not None else ''      )
												texts.append(snake_case_      )
			return {"title": titles, "text": texts}






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






def        lowerCAmelCase_       (		snake_case_		:      "RagExampleArguments"							,   snake_case_		:      "ProcessingArguments"							,   snake_case_		:      "IndexHnswArguments"							,   )       ->List[str]:
			######################################
			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__     :					List[Any]     	=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__     :					Optional[Any]     	=dataset.map(snake_case_							,   batched=snake_case_							,   num_proc=processing_args.num_proc      )

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

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

			# And save the index
			lowerCamelCase__     :					int     	=os.path.join(rag_example_args.output_dir							,   'my_knowledge_dataset_hnsw_index.faiss'      )
			dataset.get_index('embeddings'      ).save(snake_case_      )
			# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

			######################################
			logger.info('Step 3 - Load RAG'      )
			######################################

			# Easy way to load the model
			lowerCamelCase__     :					List[str]     	=RagRetriever.from_pretrained(
			    rag_example_args.rag_model_name							,   index_name='custom'							,   indexed_dataset=snake_case_      )
			lowerCamelCase__     :					List[str]     	=RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,   retriever=snake_case_      )
			lowerCamelCase__     :					Union[str, Any]     	=RagTokenizer.from_pretrained(rag_example_args.rag_model_name      )

			# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
			# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

			######################################
			logger.info('Step 4 - Have fun'      )
			######################################

			lowerCamelCase__     :					Optional[int]     	=rag_example_args.question or 'What does Moses\' rod turn into ?'
			lowerCamelCase__     :					Optional[int]     	=tokenizer.question_encoder(snake_case_							,   return_tensors='pt'      )['input_ids']
			lowerCamelCase__     :					List[str]     	=model.generate(snake_case_      )
			lowerCamelCase__     :					Optional[int]     	=tokenizer.batch_decode(snake_case_							,   skip_special_tokens=snake_case_      )[0]
			logger.info('Q: ' + question      )
			logger.info('A: ' + generated_string      )

@dataclass
class    A_     :



							"""simple docstring"""






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






@dataclass
class    A_     :



							"""simple docstring"""






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






@dataclass
class    A_     :



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       field(
							    default=768				, metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}				, )
							SCREAMING_SNAKE_CASE_     				=       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)

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

"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase								=						logging.get_logger(__name__)

lowerCAmelCase								=						{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}

class    A_     (					A__       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       """nystromformer"""





							def __init__(  self  :Optional[Any]					,							lowerCamelCase_  :Optional[Any]=30_000					,							lowerCamelCase_  :str=768					,							lowerCamelCase_  :str=12					,							lowerCamelCase_  :str=12					,							lowerCamelCase_  :Any=3_072					,							lowerCamelCase_  :Optional[int]="gelu_new"					,							lowerCamelCase_  :List[Any]=0.1					,							lowerCamelCase_  :List[str]=0.1					,							lowerCamelCase_  :Any=510					,							lowerCamelCase_  :Optional[int]=2					,							lowerCamelCase_  :List[Any]=64					,							lowerCamelCase_  :Union[str, Any]=64					,							lowerCamelCase_  :str=65					,							lowerCamelCase_  :Union[str, Any]=False					,							lowerCamelCase_  :str=0.02					,							lowerCamelCase_  :Any=1e-5					,							lowerCamelCase_  :int=1					,							lowerCamelCase_  :Optional[int]=0					,							lowerCamelCase_  :str=2					,							**lowerCamelCase_  :int					,							):





										"""simple docstring"""

										lowerCamelCase__     :					int		=vocab_size
										lowerCamelCase__     :					Optional[int]		=max_position_embeddings
										lowerCamelCase__     :					List[Any]		=hidden_size
										lowerCamelCase__     :					Tuple		=num_hidden_layers
										lowerCamelCase__     :					Optional[Any]		=num_attention_heads
										lowerCamelCase__     :					List[str]		=intermediate_size
										lowerCamelCase__     :					Union[str, Any]		=hidden_act
										lowerCamelCase__     :					int		=hidden_dropout_prob
										lowerCamelCase__     :					Tuple		=attention_probs_dropout_prob
										lowerCamelCase__     :					Dict		=initializer_range
										lowerCamelCase__     :					Tuple		=type_vocab_size
										lowerCamelCase__     :					Any		=segment_means_seq_len
										lowerCamelCase__     :					int		=num_landmarks
										lowerCamelCase__     :					Tuple		=conv_kernel_size
										lowerCamelCase__     :					Optional[int]		=inv_coeff_init_option
										lowerCamelCase__     :					List[str]		=layer_norm_eps
										super().__init__(pad_token_id=lowerCamelCase_					,							bos_token_id=lowerCamelCase_					,							eos_token_id=lowerCamelCase_					,							**lowerCamelCase_	)