
'''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,
)


__SCREAMING_SNAKE_CASE						=      logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE						=      "cuda" if torch.cuda.is_available() else "cpu"
def 					__magic_name__(       UpperCAmelCase__ :       Optional[Any]							,				UpperCAmelCase__ :       Dict=100							,				UpperCAmelCase__ :       str=" "	):
						UpperCamelCase_     :	List[str]					     =     text.split(UpperCAmelCase__	)
						return [character.join(text[i : i + n]	).strip() for i in range(0							,				len(UpperCAmelCase__	)							,				UpperCAmelCase__	)]
def 					__magic_name__(       UpperCAmelCase__ :       int	):
						UpperCamelCase_     ,UpperCamelCase_     :	str					     =     [], []
						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 					__magic_name__(       UpperCAmelCase__ :       Any							,				UpperCAmelCase__ :       Union[str, Any]							,				UpperCAmelCase__ :       Dict	):
						UpperCamelCase_     :	Tuple					     =     ctx_tokenizer(
						    documents["""title"""]							,				documents["""text"""]							,				truncation=UpperCAmelCase__							,				padding="""longest"""							,				return_tensors="""pt"""	)["""input_ids"""]
						UpperCamelCase_     :	Tuple					     =     ctx_encoder(input_ids.to(device=UpperCAmelCase__	)							,				return_dict=UpperCAmelCase__	).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}
def 					__magic_name__(       UpperCAmelCase__ :       int							,				UpperCAmelCase__ :       str							,				UpperCAmelCase__ :       Optional[Any]							,				):
						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_     :	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
						UpperCamelCase_     :	List[str]					     =     dataset.map(UpperCAmelCase__							,				batched=UpperCAmelCase__							,				num_proc=processing_args.num_proc	)

						# And compute the embeddings
						UpperCamelCase_     :	Optional[Any]					     =     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=UpperCAmelCase__	)
						UpperCamelCase_     :	int					     =     DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
						UpperCamelCase_     :	Optional[Any]					     =     Features(
						    {"""text""": Value("""string"""	), """title""": Value("""string"""	), """embeddings""": Sequence(Value("""float32"""	)	)}	)  # optional, save as float32 instead of float64 to save space
						UpperCamelCase_     :	Dict					     =     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
						UpperCamelCase_     :	Optional[int]					     =     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
						UpperCamelCase_     :	Tuple					     =     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
						UpperCamelCase_     :	str					     =     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

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

						# Easy way to load the model
						UpperCamelCase_     :	List[Any]					     =     RagRetriever.from_pretrained(
						    rag_example_args.rag_model_name							,				index_name="""custom"""							,				indexed_dataset=UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[int]					     =     RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,				retriever=UpperCAmelCase__	)
						UpperCamelCase_     :	Optional[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"""	)
						######################################

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




@dataclass
class     __snake_case			:
		a_          =					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'"}					,			)
		a_          =					field(
		    default=_a					,			metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}					,			)
		a_          =					field(
		    default="facebook/rag-sequence-nq"					,			metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}					,			)
		a_          =					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_          =					field(
		    default=_a					,			metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}					,			)







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







@dataclass
class     __snake_case			:
		a_          =					field(
		    default=768					,			metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}					,			)
		a_          =					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						=      HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	__SCREAMING_SNAKE_CASE			,      __SCREAMING_SNAKE_CASE			,      __SCREAMING_SNAKE_CASE						=      parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		__SCREAMING_SNAKE_CASE						=      rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)







from typing import Callable, Optional, Union

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


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							__UpperCAmelCase      ="""prophetnet"""
							__UpperCAmelCase      =["""past_key_values"""]
							__UpperCAmelCase      ={
							    """num_attention_heads""": """num_encoder_attention_heads""",
							}




							def __init__(			self       ,    _A = 0.1       ,    _A = "gelu"       ,    _A = 3_0_5_2_2       ,    _A = 1_0_2_4       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 4_0_9_6       ,    _A = 1_2       ,    _A = 1_6       ,    _A = 0.1       ,    _A = 0.1       ,    _A = 5_1_2       ,    _A = 0.02       ,    _A = True       ,    _A = True       ,    _A = 0       ,    _A = 2       ,    _A = 3_2       ,    _A = 1_2_8       ,    _A = False       ,    _A = 0.0       ,    _A = True       ,    _A = 0       ,    _A = 1       ,    _A = 2       ,    **_A       ,    ):
												__lowerCAmelCase       =				vocab_size
												__lowerCAmelCase       =				hidden_size
												__lowerCAmelCase       =				encoder_ffn_dim
												__lowerCAmelCase       =				num_encoder_layers
												__lowerCAmelCase       =				num_encoder_attention_heads
												__lowerCAmelCase       =				decoder_ffn_dim
												__lowerCAmelCase       =				num_decoder_layers
												__lowerCAmelCase       =				num_decoder_attention_heads
												__lowerCAmelCase       =				max_position_embeddings
												__lowerCAmelCase       =				init_std  # Normal(0, this parameter)
												__lowerCAmelCase       =				activation_function

												# parameters for prophetnet
												__lowerCAmelCase       =				ngram
												__lowerCAmelCase       =				num_buckets
												__lowerCAmelCase       =				relative_max_distance
												__lowerCAmelCase       =				disable_ngram_loss
												__lowerCAmelCase       =				eps

												# 3 Types of Dropout
												__lowerCAmelCase       =				attention_dropout
												__lowerCAmelCase       =				activation_dropout
												__lowerCAmelCase       =				dropout

												__lowerCAmelCase       =				use_cache

												super().__init__(
												    pad_token_id=_A       ,    bos_token_id=_A       ,    eos_token_id=_A       ,    is_encoder_decoder=_A       ,    add_cross_attention=_A       ,    decoder_start_token_id=_A       ,    **_A       ,    )




							@property
							def 							A__	(			self				):
												return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 							A__	(			self       ,    _A				):
												raise NotImplementedError(
												    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
												    ' `num_decoder_layers`.'				)






