
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


_lowercase											=    logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowercase											=    """cuda""" if torch.cuda.is_available() else """cpu"""
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     int=1_0_0			,							UpperCAmelCase_	:     Optional[int]=" "				)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				Dict		=text.split(UpperCAmelCase_				)
       return [character.join(text[i : i + n]				).strip() for i in range(0			,							len(UpperCAmelCase_				)			,							UpperCAmelCase_				)]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     dict				)			->						dict:
       SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=[], []
       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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     dict			,							UpperCAmelCase_	:     DPRContextEncoder			,							UpperCAmelCase_	:     DPRContextEncoderTokenizerFast				)			->						dict:
       SCREAMING_SNAKE_CASE_							:				Tuple		=ctx_tokenizer(
           documents['''title''']			,							documents['''text''']			,							truncation=UpperCAmelCase_			,							padding='''longest'''			,							return_tensors='''pt'''				)['''input_ids''']
       SCREAMING_SNAKE_CASE_							:				Dict		=ctx_encoder(input_ids.to(device=UpperCAmelCase_				)			,							return_dict=UpperCAmelCase_				).pooler_output
       return {"embeddings": embeddings.detach().cpu().numpy()}
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     "RagExampleArguments"			,							UpperCAmelCase_	:     "ProcessingArguments"			,							UpperCAmelCase_	:     "IndexHnswArguments"			,							)			->						int:
       ######################################
       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
       SCREAMING_SNAKE_CASE_							:				Union[str, 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
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=dataset.map(UpperCAmelCase_			,							batched=UpperCAmelCase_			,							num_proc=processing_args.num_proc				)

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


@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						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'"}  ,      )
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}  ,      )
       __lowerCamelCase				       =						field(
           default="facebook/rag-sequence-nq"  ,      metadata={"help": "The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"}  ,      )
       __lowerCamelCase				       =						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'"
               )
           }  ,      )
       __lowerCamelCase				       =						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 			lowercase_		:
       __lowerCamelCase				       =						field(
           default=A  ,      metadata={
               "help": "The number of processes to use to split the documents into passages. Default is single process."
           }  ,      )
       __lowerCamelCase				       =						field(
           default=1_6  ,      metadata={
               "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
           }  ,      )






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

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


from dataclasses import asdict, dataclass
from typing import Optional

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


_lowercase											=    logging.get_logger(__name__)

# TODO Update this
_lowercase											=    {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						"esm"


       def __init__(    self						,       __A=None						,       __A=None						,       __A=None						,       __A=768						,       __A=12						,       __A=12						,       __A=3_072						,       __A=0.1						,       __A=0.1						,       __A=1_026						,       __A=0.02						,       __A=1e-1_2						,       __A="absolute"						,       __A=True						,       __A=None						,       __A=False						,       __A=False						,       __A=None						,       __A=None						,       **__A						,       )						->    int:
              super().__init__(pad_token_id=__A						,       mask_token_id=__A						,       **__A				)

              SCREAMING_SNAKE_CASE_							:				Any		=vocab_size
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=hidden_size
              SCREAMING_SNAKE_CASE_							:				int		=num_hidden_layers
              SCREAMING_SNAKE_CASE_							:				int		=num_attention_heads
              SCREAMING_SNAKE_CASE_							:				List[str]		=intermediate_size
              SCREAMING_SNAKE_CASE_							:				int		=hidden_dropout_prob
              SCREAMING_SNAKE_CASE_							:				int		=attention_probs_dropout_prob
              SCREAMING_SNAKE_CASE_							:				List[Any]		=max_position_embeddings
              SCREAMING_SNAKE_CASE_							:				List[Any]		=initializer_range
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=layer_norm_eps
              SCREAMING_SNAKE_CASE_							:				str		=position_embedding_type
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=use_cache
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=emb_layer_norm_before
              SCREAMING_SNAKE_CASE_							:				Tuple		=token_dropout
              SCREAMING_SNAKE_CASE_							:				List[Any]		=is_folding_model
              if is_folding_model:
                     if esmfold_config is None:
                            logger.info('''No esmfold_config supplied for folding model, using default values.'''				)
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=EsmFoldConfig()
                     elif isinstance(__A						,       __A				):
                            SCREAMING_SNAKE_CASE_							:				List[Any]		=EsmFoldConfig(**__A				)
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=esmfold_config
                     if vocab_list is None:
                            logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''				)
                            SCREAMING_SNAKE_CASE_							:				Tuple		=get_default_vocab_list()
                     else:
                            SCREAMING_SNAKE_CASE_							:				Optional[Any]		=vocab_list
              else:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=None
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=None
              if self.esmfold_config is not None and getattr(self.esmfold_config						,       '''use_esm_attn_map'''						,       __A				):
                     raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''				)





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		=super().to_dict()
              if isinstance(self.esmfold_config						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.esmfold_config.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						None
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						0

       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						False

       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						None


       def 	_snake_case				(    self				)						->    Optional[Any]:
              if self.trunk is None:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=TrunkConfig()
              elif isinstance(self.trunk						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				List[str]		=TrunkConfig(**self.trunk				)





       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				int		=asdict(self				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.trunk.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						4_8
       __lowerCamelCase				       =						1_0_2_4
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						3_2
       __lowerCamelCase				       =						0
       __lowerCamelCase				       =						0
       __lowerCamelCase				       =						False
       __lowerCamelCase				       =						4
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						None


       def 	_snake_case				(    self				)						->    int:
              if self.structure_module is None:
                     SCREAMING_SNAKE_CASE_							:				Dict		=StructureModuleConfig()
              elif isinstance(self.structure_module						,       __A				):
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=StructureModuleConfig(**self.structure_module				)

              if self.max_recycles <= 0:
                     raise ValueError(F'`max_recycles` should be positive, got {self.max_recycles}.'				)
              if self.sequence_state_dim % self.sequence_state_dim != 0:
                     raise ValueError(
                         '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
                         F' {self.sequence_state_dim} and {self.sequence_state_dim}.'				)
              if self.pairwise_state_dim % self.pairwise_state_dim != 0:
                     raise ValueError(
                         '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
                         F' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'				)

              SCREAMING_SNAKE_CASE_							:				Tuple		=self.sequence_state_dim // self.sequence_head_width
              SCREAMING_SNAKE_CASE_							:				str		=self.pairwise_state_dim // self.pairwise_head_width

              if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
                     raise ValueError(
                         '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
                         F' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'				)
              if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
                     raise ValueError(
                         '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
                         F' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'				)
              if self.pairwise_state_dim % 2 != 0:
                     raise ValueError(F'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'				)

              if self.dropout >= 0.4:
                     raise ValueError(F'`dropout` should not be greater than 0.4, got {self.dropout}.'				)





       def 	_snake_case				(    self				)						->    int:
              SCREAMING_SNAKE_CASE_							:				str		=asdict(self				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=self.structure_module.to_dict()
              return output






@dataclass
class 			lowercase_		:
       __lowerCamelCase				       =						3_8_4
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						1_6
       __lowerCamelCase				       =						1_2_8
       __lowerCamelCase				       =						1_2
       __lowerCamelCase				       =						4
       __lowerCamelCase				       =						8
       __lowerCamelCase				       =						0.1
       __lowerCamelCase				       =						8
       __lowerCamelCase				       =						1
       __lowerCamelCase				       =						2
       __lowerCamelCase				       =						7
       __lowerCamelCase				       =						1_0
       __lowerCamelCase				       =						1E-8
       __lowerCamelCase				       =						1E5


       def 	_snake_case				(    self				)						->    str:
              return asdict(self				)






def 							SCREAMING_SNAKE_CASE_	(					)			->						int:
       return (
           "<cls>",
           "<pad>",
           "<eos>",
           "<unk>",
           "L",
           "A",
           "G",
           "V",
           "S",
           "E",
           "R",
           "T",
           "I",
           "D",
           "P",
           "K",
           "Q",
           "N",
           "F",
           "Y",
           "M",
           "H",
           "W",
           "C",
           "X",
           "B",
           "U",
           "Z",
           "O",
           ".",
           "-",
           "<null_1>",
           "<mask>",
       )
