

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							:  Any     =				logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCamelCase							:  Tuple     =				"cuda" if torch.cuda.is_available() else "cpu"

def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     Optional[Any]=100							,					UpperCAmelCase				:     List[Any]=" "     )    ->		List[str]:
  UpperCAmelCase			:				int										=					text.split(UpperCAmelCase     )
  return [character.join(text[i : i + n]     ).strip() for i in range(0							,					len(UpperCAmelCase     )							,					UpperCAmelCase     )]

def      a__	(						UpperCAmelCase				:     dict     )    ->		dict:
  UpperCAmelCase					,		UpperCAmelCase			:				List[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      a__	(						UpperCAmelCase				:     dict							,					UpperCAmelCase				:     DPRContextEncoder							,					UpperCAmelCase				:     DPRContextEncoderTokenizerFast     )    ->		dict:
  UpperCAmelCase			:				List[str]										=					ctx_tokenizer(
      documents['''title''']							,					documents['''text''']							,					truncation=UpperCAmelCase							,					padding='''longest'''							,					return_tensors='''pt'''     )['''input_ids''']
  UpperCAmelCase			:				Optional[Any]										=					ctx_encoder(input_ids.to(device=UpperCAmelCase     )							,					return_dict=UpperCAmelCase     ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

def      a__	(						UpperCAmelCase				:     "RagExampleArguments"							,					UpperCAmelCase				:     "ProcessingArguments"							,					UpperCAmelCase				:     "IndexHnswArguments"							,					)    ->		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[str]										=					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			:				str										=					dataset.map(UpperCAmelCase							,					batched=UpperCAmelCase							,					num_proc=processing_args.num_proc     )

  # And compute the embeddings
  UpperCAmelCase			:				Dict										=					DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=UpperCAmelCase     )
  UpperCAmelCase			:				Optional[Any]										=					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			:				Optional[int]										=					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			:				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
  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			:				List[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			:				Optional[int]										=					RagRetriever.from_pretrained(
      rag_example_args.rag_model_name							,					index_name='''custom'''							,					indexed_dataset=UpperCAmelCase     )
  UpperCAmelCase			:				Any										=					RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name							,					retriever=UpperCAmelCase     )
  UpperCAmelCase			:				Dict										=					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			:				Tuple										=					rag_example_args.question or '''What does Moses\' rod turn into ?'''
  UpperCAmelCase			:				str										=					tokenizer.question_encoder(UpperCAmelCase							,					return_tensors='''pt'''     )['''input_ids''']
  UpperCAmelCase			:				Tuple										=					model.generate(UpperCAmelCase     )
  UpperCAmelCase			:				Dict										=					tokenizer.batch_decode(UpperCAmelCase							,					skip_special_tokens=UpperCAmelCase     )[0]
  logger.info('''Q: ''' + question     )
  logger.info('''A: ''' + generated_string     )





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






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






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

     _lowerCamelCase							:  int     =				HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
     _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase							:  Optional[Any]     =				parser.parse_args_into_dataclasses()
     with TemporaryDirectory() as tmp_dir:
          _lowerCamelCase							:  int     =				rag_example_args.output_dir or tmp_dir
          main(rag_example_args, processing_args, index_hnsw_args)








from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_lowerCamelCase							:  Union[str, Any]     =				logging.get_logger(__name__)

_lowerCamelCase							:  Optional[int]     =				{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}





class    __UpperCAmelCase	(			lowerCamelCase__		):
   UpperCamelCase  	=	"""m2m_100"""
   UpperCamelCase  	=	["""past_key_values"""]
   UpperCamelCase  	=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



   def __init__(				self				:	int,						__A				:	Optional[Any]=1_2_8_1_1_2,						__A				:	str=1_0_2_4,						__A				:	Tuple=1_2,						__A				:	Any=4_0_9_6,						__A				:	str=1_6,						__A				:	List[str]=1_2,						__A				:	int=4_0_9_6,						__A				:	List[Any]=1_6,						__A				:	Tuple=0.0_5,						__A				:	Tuple=0.0_5,						__A				:	List[Any]=True,						__A				:	int=True,						__A				:	str="relu",						__A				:	str=1_0_2_4,						__A				:	str=0.1,						__A				:	str=0.1,						__A				:	List[str]=0.0,						__A				:	List[str]=0.0_2,						__A				:	Any=2,						__A				:	List[str]=True,						__A				:	Tuple=1,						__A				:	Optional[Any]=0,						__A				:	Optional[Any]=2,						**__A				:	Optional[Any],						):
     UpperCAmelCase			:				str										=					vocab_size
     UpperCAmelCase			:				Optional[Any]										=					max_position_embeddings
     UpperCAmelCase			:				List[str]										=					d_model
     UpperCAmelCase			:				Any										=					encoder_ffn_dim
     UpperCAmelCase			:				Optional[int]										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					encoder_attention_heads
     UpperCAmelCase			:				str										=					decoder_ffn_dim
     UpperCAmelCase			:				Any										=					decoder_layers
     UpperCAmelCase			:				List[str]										=					decoder_attention_heads
     UpperCAmelCase			:				str										=					dropout
     UpperCAmelCase			:				int										=					attention_dropout
     UpperCAmelCase			:				Dict										=					activation_dropout
     UpperCAmelCase			:				Tuple										=					activation_function
     UpperCAmelCase			:				Any										=					init_std
     UpperCAmelCase			:				Union[str, Any]										=					encoder_layerdrop
     UpperCAmelCase			:				List[Any]										=					decoder_layerdrop
     UpperCAmelCase			:				Optional[Any]										=					use_cache
     UpperCAmelCase			:				str										=					encoder_layers
     UpperCAmelCase			:				List[Any]										=					scale_embedding  # scale factor will be sqrt(d_model) if True

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






class    __UpperCAmelCase	(			lowerCamelCase__		):



   @property
   def 			__magic_name__       (				self				:	List[str]		):
     UpperCAmelCase			:				List[Any]										=					OrderedDict(
         [
             ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
             ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
         ]		)

     if self.use_past:
       UpperCAmelCase			:				List[str]										=					{0: '''batch'''}
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
     else:
       UpperCAmelCase			:				Any										=					{0: '''batch''', 1: '''decoder_sequence'''}
       UpperCAmelCase			:				List[Any]										=					{0: '''batch''', 1: '''decoder_sequence'''}

     if self.use_past:
       self.fill_with_past_key_values_(__A,						direction='''inputs'''		)
     return common_inputs



   def 			__magic_name__       (				self				:	Dict,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     UpperCAmelCase			:				List[str]										=					compute_effective_axis_dimension(
         __A,						fixed_dimension=OnnxConfig.default_fixed_batch,						num_token_to_add=0		)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     UpperCAmelCase			:				Optional[int]										=					tokenizer.num_special_tokens_to_add(__A		)
     UpperCAmelCase			:				int										=					compute_effective_axis_dimension(
         __A,						fixed_dimension=OnnxConfig.default_fixed_sequence,						num_token_to_add=__A		)

     # Generate dummy inputs according to compute batch and sequence
     UpperCAmelCase			:				List[str]										=					[''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
     UpperCAmelCase			:				Tuple										=					dict(tokenizer(__A,						return_tensors=__A		)		)
     return common_inputs



   def 			__magic_name__       (				self				:	Any,						__A				:	PreTrainedTokenizer,						__A				:	int = -1,						__A				:	int = -1,						__A				:	bool = False,						__A				:	Optional[TensorType] = None,						):
     UpperCAmelCase			:				int										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)

     # Generate decoder inputs
     UpperCAmelCase			:				Union[str, Any]										=					seq_length if not self.use_past else 1
     UpperCAmelCase			:				Optional[int]										=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         __A,						__A,						__A,						__A,						__A		)
     UpperCAmelCase			:				Dict										=					{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
     UpperCAmelCase			:				Optional[int]										=					dict(**__A,						**__A		)

     if self.use_past:
       if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''		)
       else:
         import torch
       UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					common_inputs['''input_ids'''].shape
       UpperCAmelCase			:				Dict										=					common_inputs['''decoder_input_ids'''].shape[1]
       UpperCAmelCase					,		UpperCAmelCase			:				Dict										=					self.num_attention_heads
       UpperCAmelCase			:				Union[str, Any]										=					(
           batch,
           num_encoder_attention_heads,
           encoder_seq_length,
           self._config.hidden_size // num_encoder_attention_heads,
       )
       UpperCAmelCase			:				str										=					decoder_seq_length + 3
       UpperCAmelCase			:				Union[str, Any]										=					(
           batch,
           num_decoder_attention_heads,
           decoder_past_length,
           self._config.hidden_size // num_decoder_attention_heads,
       )

       UpperCAmelCase			:				Union[str, Any]										=					torch.cat(
           [common_inputs['''decoder_attention_mask'''], torch.ones(__A,						__A		)],						dim=1		)

       UpperCAmelCase			:				Any										=					[]
       # If the number of encoder and decoder layers are present in the model configuration, both are considered
       UpperCAmelCase					,		UpperCAmelCase			:				str										=					self.num_layers
       UpperCAmelCase			:				Any										=					min(__A,						__A		)
       UpperCAmelCase			:				Dict										=					max(__A,						__A		) - min_num_layers
       UpperCAmelCase			:				Tuple										=					'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

       for _ in range(__A		):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(__A		),
                 torch.zeros(__A		),
                 torch.zeros(__A		),
                 torch.zeros(__A		),
             )		)
       # TODO: test this.
       UpperCAmelCase			:				List[str]										=					encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
       for _ in range(__A,						__A		):
         common_inputs["past_key_values"].append((torch.zeros(__A		), torch.zeros(__A		))		)
     return common_inputs





   UpperCamelCase  	=	_generate_dummy_inputs_for_default_and_seqaseq_lm





