






"""simple docstring"""




import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
 import tensorflow as tf

 tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset


_snake_case					   =     5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  return PTtoTFCommand(
      args.model_name  ,     args.local_dir  ,     args.max_error  ,     args.new_weights  ,     args.no_pr  ,     args.push  ,     args.extra_commit_description  ,     args.override_model_class  ,     )






class  UpperCamelCase    (			snake_case_   ):


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		ArgumentParser							)      -> Dict:
   _a						: Optional[int]    =			parser.add_parser(
       """pt-to-tf"""							,		help=(
           """CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint."""
           """ Can also be used to validate existing weights without opening PRs, with --no-pr."""
       )							,		)
   train_parser.add_argument(
       """--model-name"""							,		type=UpperCAmelCase__							,		required=UpperCAmelCase__							,		help="""The model name, including owner/organization, as seen on the hub."""							,		)
   train_parser.add_argument(
       """--local-dir"""							,		type=UpperCAmelCase__							,		default=""""""							,		help="""Optional local directory of the model repository. Defaults to /tmp/{model_name}"""							,		)
   train_parser.add_argument(
       """--max-error"""							,		type=UpperCAmelCase__							,		default=UpperCAmelCase__							,		help=(
           f"""Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk."""
       )							,		)
   train_parser.add_argument(
       """--new-weights"""							,		action="""store_true"""							,		help="""Optional flag to create new TensorFlow weights, even if they already exist."""							,		)
   train_parser.add_argument(
       """--no-pr"""							,		action="""store_true"""							,		help="""Optional flag to NOT open a PR with converted weights."""							)
   train_parser.add_argument(
       """--push"""							,		action="""store_true"""							,		help="""Optional flag to push the weights directly to `main` (requires permissions)"""							,		)
   train_parser.add_argument(
       """--extra-commit-description"""							,		type=UpperCAmelCase__							,		default=""""""							,		help="""Optional additional commit description to use when opening a PR (e.g. to tag the owner)."""							,		)
   train_parser.add_argument(
       """--override-model-class"""							,		type=UpperCAmelCase__							,		default=UpperCAmelCase__							,		help="""If you think you know better than the auto-detector, you can specify the model class here. """
       """Can be either an AutoModel class or a specific model class like BertForSequenceClassification."""							,		)
   train_parser.set_defaults(func=UpperCAmelCase__							)


 @staticmethod
 def _lowercase			(  UpperCAmelCase__	:		Optional[int]							,		UpperCAmelCase__	:		Any							)      -> List[Any]:
   _a						: Optional[Any]    =			set(pt_outputs.keys()							)
   _a						: List[str]    =			set(tf_outputs.keys()							)
   if pt_out_attrs != tf_out_attrs:
     raise ValueError(
         f"""The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:"""
         f""" {tf_out_attrs})"""							)

   # 2. For each output attribute, computes the difference
   def _find_pt_tf_differences(UpperCAmelCase__	:		int							,		UpperCAmelCase__	:		Optional[int]							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		int=""							):
     # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
     # recursivelly, keeping the name of the attribute.
     if isinstance(UpperCAmelCase__							,		torch.Tensor							):
       _a						: List[str]    =			np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
       _a						: Optional[Any]    =			tensor_difference
     else:
       _a						: Dict    =			attr_name
       for i, pt_item in enumerate(UpperCAmelCase__							):
         # If it is a named attribute, we keep the name. Otherwise, just its index.
         if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							):
           _a						: int    =			root_name + pt_item
           _a						: List[str]    =			tf_out[pt_item]
           _a						: int    =			pt_out[pt_item]
         else:
           _a						: int    =			root_name + f"""[{i}]"""
           _a						: Optional[int]    =			tf_out[i]
         _a						: Optional[Any]    =			_find_pt_tf_differences(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							)

     return differences

   return _find_pt_tf_differences(UpperCAmelCase__							,		UpperCAmelCase__							,		{}							)


 def __init__(  self	:		str							,		UpperCAmelCase__	:		str							,		UpperCAmelCase__	:		str							,		UpperCAmelCase__	:		float							,		UpperCAmelCase__	:		bool							,		UpperCAmelCase__	:		bool							,		UpperCAmelCase__	:		bool							,		UpperCAmelCase__	:		str							,		UpperCAmelCase__	:		str							,		*UpperCAmelCase__	:		Tuple							,		)      -> Union[str, Any]:
   _a						: Any    =			logging.get_logger("""transformers-cli/pt_to_tf"""							)
   _a						: Dict    =			model_name
   _a						: Optional[int]    =			local_dir if local_dir else os.path.join("""/tmp"""							,		UpperCAmelCase__							)
   _a						: Tuple    =			max_error
   _a						: List[str]    =			new_weights
   _a						: List[Any]    =			no_pr
   _a						: Dict    =			push
   _a						: List[str]    =			extra_commit_description
   _a						: Any    =			override_model_class


 def _lowercase			(  self	:		Any							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		str							,		UpperCAmelCase__	:		Union[str, Any]							)      -> str:

   def _get_audio_input():
     _a						: Dict    =			load_dataset("""hf-internal-testing/librispeech_asr_dummy"""							,		"""clean"""							,		split="""validation"""							)
     _a						: Dict    =			ds.sort("""id"""							).select(range(2							)							)[:2]["""audio"""]
     _a						: Any    =			[x["""array"""] for x in speech_samples]
     return raw_samples

   _a						: str    =			type(pt_model.config							)
   if model_config_class in PROCESSOR_MAPPING:
     _a						: Optional[Any]    =			AutoProcessor.from_pretrained(self._local_dir							)
     if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
       _a						: str    =			processor.tokenizer.eos_token
   elif model_config_class in IMAGE_PROCESSOR_MAPPING:
     _a						: Optional[Any]    =			AutoImageProcessor.from_pretrained(self._local_dir							)
   elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
     _a						: List[str]    =			AutoFeatureExtractor.from_pretrained(self._local_dir							)
   elif model_config_class in TOKENIZER_MAPPING:
     _a						: Optional[int]    =			AutoTokenizer.from_pretrained(self._local_dir							)
     if processor.pad_token is None:
       _a						: Optional[Any]    =			processor.eos_token
   else:
     raise ValueError(f"""Unknown data processing type (model config type: {model_config_class})"""							)

   _a						: int    =			set(inspect.signature(pt_model.forward							).parameters.keys()							)
   _a						: List[Any]    =			{}
   if "input_ids" in model_forward_signature:
     processor_inputs.update(
         {
             """text""": ["""Hi there!""", """I am a batch with more than one row and different input lengths."""],
             """padding""": True,
             """truncation""": True,
         }							)
   if "pixel_values" in model_forward_signature:
     _a						: Optional[int]    =			load_dataset("""cifar10"""							,		"""plain_text"""							,		split="""test"""							)[:2]["""img"""]
     processor_inputs.update({"""images""": sample_images}							)
   if "input_features" in model_forward_signature:
     _a						: List[Any]    =			inspect.signature(processor.feature_extractor							).parameters
     # Pad to the largest input length by default but take feature extractor default
     # padding value if it exists e.g. "max_length" and is not False or None
     if "padding" in feature_extractor_signature:
       _a						: Any    =			feature_extractor_signature["""padding"""].default
       if default_strategy is not False and default_strategy is not None:
         _a						: Dict    =			default_strategy
       else:
         _a						: str    =			True
     else:
       _a						: List[Any]    =			True
     processor_inputs.update({"""audio""": _get_audio_input(), """padding""": padding_strategy}							)
   if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
     processor_inputs.update({"""audio""": _get_audio_input(), """padding""": True}							)
   _a						: Optional[int]    =			processor(**UpperCAmelCase__							,		return_tensors="""pt"""							)
   _a						: Optional[Any]    =			processor(**UpperCAmelCase__							,		return_tensors="""tf"""							)

   # Extra input requirements, in addition to the input modality
   if (
       config.is_encoder_decoder
       or (hasattr(UpperCAmelCase__							,		"""encoder"""							) and hasattr(UpperCAmelCase__							,		"""decoder"""							))
       or "decoder_input_ids" in tf_dummy_inputs
   ):
     _a						: str    =			np.asarray([[1], [1]]							,		dtype=UpperCAmelCase__							) * (pt_model.config.decoder_start_token_id or 0)
     pt_input.update({"""decoder_input_ids""": torch.tensor(UpperCAmelCase__							)}							)
     tf_input.update({"""decoder_input_ids""": tf.convert_to_tensor(UpperCAmelCase__							)}							)

   return pt_input, tf_input







 def _lowercase			(  self	:		Optional[int]							)      -> Union[str, Any]:
   # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
   if version.parse(huggingface_hub.__version__							) < version.parse("""0.9.0"""							):
     raise ImportError(
         """The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub"""
         """ installation."""							)
   else:
     from huggingface_hub import Repository, create_commit
     from huggingface_hub._commit_api import CommitOperationAdd

   # Fetch remote data
   _a						: Any    =			Repository(local_dir=self._local_dir							,		clone_from=self._model_name							)

   # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
   _a						: Optional[Any]    =			AutoConfig.from_pretrained(self._local_dir							)
   _a						: Optional[int]    =			config.architectures
   if self._override_model_class is not None:
     if self._override_model_class.startswith("""TF"""							):
       _a						: List[str]    =			[self._override_model_class[2:]]
     else:
       _a						: Optional[int]    =			[self._override_model_class]
     try:
       _a						: str    =			getattr(import_module("""transformers"""							)							,		architectures[0]							)
     except AttributeError:
       raise ValueError(f"""Model class {self._override_model_class} not found in transformers."""							)
     try:
       _a						: int    =			getattr(import_module("""transformers"""							)							,		"""TF""" + architectures[0]							)
     except AttributeError:
       raise ValueError(f"""TF model class TF{self._override_model_class} not found in transformers."""							)
   elif architectures is None:  # No architecture defined -- use auto classes
     _a						: List[Any]    =			getattr(import_module("""transformers"""							)							,		"""AutoModel"""							)
     _a						: Union[str, Any]    =			getattr(import_module("""transformers"""							)							,		"""TFAutoModel"""							)
     self._logger.warning("""No detected architecture, using AutoModel/TFAutoModel"""							)
   else:  # Architecture defined -- use it
     if len(UpperCAmelCase__							) > 1:
       raise ValueError(f"""More than one architecture was found, aborting. (architectures = {architectures})"""							)
     self._logger.warning(f"""Detected architecture: {architectures[0]}"""							)
     _a						: List[Any]    =			getattr(import_module("""transformers"""							)							,		architectures[0]							)
     try:
       _a						: Any    =			getattr(import_module("""transformers"""							)							,		"""TF""" + architectures[0]							)
     except AttributeError:
       raise AttributeError(f"""The TensorFlow equivalent of {architectures[0]} doesn't exist in transformers."""							)

        # Check the TF dummy inputs to see what keys we need in the forward pass
   _a						: Optional[int]    =			tf_class.from_config(UpperCAmelCase__							)
   _a						: int    =			tf_from_pt_model.dummy_inputs

   del tf_from_pt_model  # Try to keep only one model in memory at a time

   # Load the model and get some basic inputs
   _a						: List[str]    =			pt_class.from_pretrained(self._local_dir							)
   pt_model.eval()

   _a   ,						_a						: int    =			self.get_inputs(UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							)

   with torch.no_grad():
     _a						: List[str]    =			pt_model(**UpperCAmelCase__							,		output_hidden_states=UpperCAmelCase__							)
   del pt_model  # will no longer be used, and may have a large memory footprint

   _a						: int    =			tf_class.from_pretrained(self._local_dir							,		from_pt=UpperCAmelCase__							)
   _a						: Optional[Any]    =			tf_from_pt_model(**UpperCAmelCase__							,		output_hidden_states=UpperCAmelCase__							,		training=UpperCAmelCase__							)

   # Confirms that cross loading PT weights into TF worked.
   _a						: List[Any]    =			self.find_pt_tf_differences(UpperCAmelCase__							,		UpperCAmelCase__							)
   _a						: List[Any]    =			{k: v for k, v in crossload_differences.items() if """hidden""" not in k}
   _a						: int    =			{k: v for k, v in crossload_differences.items() if """hidden""" in k}
   if len(UpperCAmelCase__							) == 0 and architectures is not None:
     raise ValueError(
         f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
         """ output was found. All outputs start with 'hidden'"""							)
   _a						: List[Any]    =			max(output_differences.values()							) if output_differences else 0.0
   _a						: Any    =			max(hidden_differences.values()							)
   if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
     raise ValueError(
         """The cross-loaded TensorFlow model has different outputs, something went wrong!\n"""
         + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
         + """\n""".join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]							)
         + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
         + """\n""".join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]							)							)

   # Save the weights in a TF format (if needed) and confirms that the results are still good
   _a						: List[str]    =			os.path.join(self._local_dir							,		UpperCAmelCase__							)
   _a						: str    =			os.path.join(self._local_dir							,		UpperCAmelCase__							)
   if (not os.path.exists(UpperCAmelCase__							) and not os.path.exists(UpperCAmelCase__							)) or self._new_weights:
     tf_from_pt_model.save_pretrained(self._local_dir							)
   del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

   _a						: Any    =			tf_class.from_pretrained(self._local_dir							)
   _a						: Union[str, Any]    =			tf_model(**UpperCAmelCase__							,		output_hidden_states=UpperCAmelCase__							)

   _a						: List[str]    =			self.find_pt_tf_differences(UpperCAmelCase__							,		UpperCAmelCase__							)
   _a						: Optional[Any]    =			{k: v for k, v in conversion_differences.items() if """hidden""" not in k}
   _a						: List[Any]    =			{k: v for k, v in conversion_differences.items() if """hidden""" in k}
   if len(UpperCAmelCase__							) == 0 and architectures is not None:
     raise ValueError(
         f"""Something went wrong -- the config file has architectures ({architectures}), but no model head"""
         """ output was found. All outputs start with 'hidden'"""							)
   _a						: Optional[Any]    =			max(output_differences.values()							) if output_differences else 0.0
   _a						: Tuple    =			max(hidden_differences.values()							)
   if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
     raise ValueError(
         """The converted TensorFlow model has different outputs, something went wrong!\n"""
         + f"""\nList of maximum output differences above the threshold ({self._max_error}):\n"""
         + """\n""".join([f"""{k}: {v:.3e}""" for k, v in output_differences.items() if v > self._max_error]							)
         + f"""\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n"""
         + """\n""".join([f"""{k}: {v:.3e}""" for k, v in hidden_differences.items() if v > self._max_error]							)							)

   _a						: Optional[Any]    =			"""Update TF weights""" if self._new_weights else """Add TF weights"""
   if self._push:
     repo.git_add(auto_lfs_track=UpperCAmelCase__							)
     repo.git_commit(UpperCAmelCase__							)
     repo.git_push(blocking=UpperCAmelCase__							)  # this prints a progress bar with the upload
     self._logger.warning(f"""TF weights pushed into {self._model_name}"""							)
   elif not self._no_pr:
     self._logger.warning("""Uploading the weights into a new PR..."""							)
     _a						: Optional[int]    =			(
         """Model converted by the [`transformers`' `pt_to_tf`"""
         """ CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). """
         """All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n"""
         f"""Maximum crossload output difference={max_crossload_output_diff:.3e}; """
         f"""Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n"""
         f"""Maximum conversion output difference={max_conversion_output_diff:.3e}; """
         f"""Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n"""
     )
     if self._max_error > MAX_ERROR:
       commit_descrition += (
           f"""\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!"""
       )
     if self._extra_commit_description:
       commit_descrition += "\n\n" + self._extra_commit_description

     # sharded model -> adds all related files (index and .h5 shards)
     if os.path.exists(UpperCAmelCase__							):
       _a						: str    =			[
           CommitOperationAdd(path_in_repo=UpperCAmelCase__							,		path_or_fileobj=UpperCAmelCase__							)
       ]
       for shard_path in tf.io.gfile.glob(self._local_dir + """/tf_model-*.h5"""							):
         operations += [
             CommitOperationAdd(path_in_repo=os.path.basename(UpperCAmelCase__							)							,		path_or_fileobj=UpperCAmelCase__							)
         ]
     else:
       _a						: Dict    =			[CommitOperationAdd(path_in_repo=UpperCAmelCase__							,		path_or_fileobj=UpperCAmelCase__							)]

     _a						: Tuple    =			create_commit(
         repo_id=self._model_name							,		operations=UpperCAmelCase__							,		commit_message=UpperCAmelCase__							,		commit_description=UpperCAmelCase__							,		repo_type="""model"""							,		create_pr=UpperCAmelCase__							,		).pr_url
     self._logger.warning(f"""PR open in {hub_pr_url}"""							)









"""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,
)


_snake_case					   =     logging.getLogger(__name__)
torch.set_grad_enabled(False)
_snake_case					   =     'cuda' if torch.cuda.is_available() else 'cpu'

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=1_0_0  ,     UpperCamelCase__=" "       ):



  '''simple docstring'''
  _a						: str    =			text.split(UpperCamelCase__       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0  ,     len(UpperCamelCase__       )  ,     UpperCamelCase__       )]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a   ,						_a						: Optional[int]    =			[], []
  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     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



  '''simple docstring'''
  _a						: List[str]    =			ctx_tokenizer(
      documents["""title"""]  ,     documents["""text"""]  ,     truncation=UpperCamelCase__  ,     padding="""longest"""  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Optional[int]    =			ctx_encoder(input_ids.to(device=UpperCamelCase__       )  ,     return_dict=UpperCamelCase__       ).pooler_output
  return {"embeddings": embeddings.detach().cpu().numpy()}

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     ):



  '''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
  _a						: Dict    =			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
  _a						: Any    =			dataset.map(UpperCamelCase__  ,     batched=UpperCamelCase__  ,     num_proc=processing_args.num_proc       )

  # And compute the embeddings
  _a						: Optional[Any]    =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=UpperCamelCase__       )
  _a						: str    =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
  _a						: int    =			Features(
      {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
  _a						: str    =			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
  _a						: List[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
  _a						: List[str]    =			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
  _a						: 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
  _a						: str    =			RagRetriever.from_pretrained(
      rag_example_args.rag_model_name  ,     index_name="""custom"""  ,     indexed_dataset=UpperCamelCase__       )
  _a						: Any    =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,     retriever=UpperCamelCase__       )
  _a						: List[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"""       )
  ######################################

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






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







@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : Optional[int]    =      field(
     default=snake_case_	,  metadata={
         '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
     }	,  )
 UpperCamelCase      : int    =      field(
     default=16	,  metadata={
         '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
     }	,  )







@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : int    =      field(
     default=768	,  metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}	,  )
 UpperCamelCase      : int    =      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)

 _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)

