

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_							)	->				str:





  '''simple docstring'''





  for attribute in key.split("""."""							):
    snake_case_       						=      getattr(lowercase_			,					lowercase_							)

  if weight_type is not None:
    snake_case_       						=      getattr(lowercase_			,					lowercase_							).shape
  else:
    snake_case_       						=      hf_pointer.shape

  assert hf_shape == value.shape, (
      f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
      f''' {value.shape} for {full_name}'''
  )

  if weight_type == "weight":
    snake_case_       						=      value
  elif weight_type == "weight_g":
    snake_case_       						=      value
  elif weight_type == "weight_v":
    snake_case_       						=      value
  elif weight_type == "bias":
    snake_case_       						=      value
  else:
    snake_case_       						=      value

  logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''							)



def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				Dict:





  '''simple docstring'''





  snake_case_       						=      []
  snake_case_       						=      fairseq_model.state_dict()

  snake_case_       						=      hf_model.feature_extractor

  for name, value in fairseq_dict.items():
    snake_case_       						=      False
    if "conv_layers" in name:
      load_conv_layer(
          lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					hf_model.config.feat_extract_norm == """group"""			,					)
      snake_case_       						=      True
    else:
      for key, mapped_key in MAPPING.items():
        snake_case_       						=      mapped_key

        if key in name:
          snake_case_       						=      True
          if "*" in mapped_key:
            snake_case_       						=      name.split(lowercase_							)[0].split("""."""							)[-2]
            snake_case_       						=      mapped_key.replace("""*"""			,					lowercase_							)
          if "weight_g" in name:
            snake_case_       						=      """weight_g"""
          elif "weight_v" in name:
            snake_case_       						=      """weight_v"""
          elif "weight" in name:
            snake_case_       						=      """weight"""
          elif "bias" in name:
            snake_case_       						=      """bias"""
          else:
            snake_case_       						=      None
          set_recursively(lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_							)
        continue
    if not is_used:
      unused_weights.append(lowercase_							)

  logger.warning(f'''Unused weights: {unused_weights}'''							)



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_			,					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  snake_case_       						=      full_name.split("""conv_layers."""							)[-1]
  snake_case_       						=      name.split("""."""							)
  snake_case_       						=      int(items[0]							)
  snake_case_       						=      int(items[1]							)

  if type_id == 0:
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
      )
      snake_case_       						=      value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
      )
      snake_case_       						=      value
      logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
          f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
          " found."
      )
      snake_case_       						=      value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
    elif "weight" in name:
      assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
          f'''{full_name} has size {value.shape}, but'''
          f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
      )
      snake_case_       						=      value
      logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
  else:
    unused_weights.append(lowercase_							)



def    UpperCamelCase(					lowercase_							)	->				Optional[int]:





  '''simple docstring'''





  snake_case_       						=      HubertConfig()
  snake_case_       						=      model.config

  snake_case_       						=      fs_config.activation_dropout
  snake_case_       						=      False
  snake_case_       						=      fs_config.attention_dropout
  snake_case_       						=      False
  snake_case_       						=      eval(fs_config.extractor_conv_feature_layers							)
  snake_case_       						=      [x[0] for x in conv_layers]
  snake_case_       						=      [x[1] for x in conv_layers]
  snake_case_       						=      [x[2] for x in conv_layers]
  snake_case_       						=      """gelu"""
  snake_case_       						=      """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
  snake_case_       						=      False
  snake_case_       						=      0.0
  snake_case_       						=      0.0
  snake_case_       						=      fs_config.activation_fn
  snake_case_       						=      fs_config.dropout
  snake_case_       						=      fs_config.encoder_embed_dim
  snake_case_       						=      0.02
  snake_case_       						=      fs_config.encoder_ffn_embed_dim
  snake_case_       						=      1e-5
  snake_case_       						=      0.0
  snake_case_       						=      fs_config.encoder_attention_heads
  snake_case_       						=      fs_config.conv_pos_groups
  snake_case_       						=      fs_config.conv_pos
  snake_case_       						=      len(lowercase_							)
  snake_case_       						=      fs_config.encoder_layers

  return config



@torch.no_grad()
def    UpperCamelCase(					lowercase_			,					lowercase_=None							)	->				int:





  '''simple docstring'''





  snake_case_       						=      distilhubert().model.model

  if config_path is not None:
    snake_case_       						=      HubertConfig.from_pretrained(lowercase_							)
  else:
    snake_case_       						=      convert_config(lowercase_							)
  snake_case_       						=      model.eval()

  snake_case_       						=      WavaVecaFeatureExtractor(
      feature_size=1			,					sampling_rate=16000			,					padding_value=0			,					do_normalize=lowercase_			,					return_attention_mask=lowercase_			,					)
  snake_case_       						=      HubertModel(lowercase_							)

  recursively_load_weights(lowercase_			,					lowercase_							)

  feature_extractor.save_pretrained(lowercase_							)
  hf_model.save_pretrained(lowercase_							)


if __name__ == "__main__":
    lowerCamelCase_    =					argparse.ArgumentParser()
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    lowerCamelCase_    =					parser.parse_args()
    convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
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						:    Optional[Any]     = logging.getLogger(__name__)
torch.set_grad_enabled(False)
__SCREAMING_SNAKE_CASE						:    Any     = 'cuda' if torch.cuda.is_available() else 'cpu'
def 					snake_case						(__lowercase					,			__lowercase=100					,			__lowercase=" "						)       ->       List[str]:

						'''simple docstring'''



						_snake_case						:     Union[str, Any]				=    text.split(__lowercase						)
						return [character.join(text[i : i + n]						).strip() for i in range(0					,			len(__lowercase						)					,			__lowercase						)]
def 					snake_case						(__lowercase						)       ->       dict:

						'''simple docstring'''



						_snake_case				,_snake_case						:     str				=    [], []
						for title, text in zip(documents["title"]					,			documents["text"]						):
												if text is not None:
																		for passage in split_text(__lowercase						):
																								titles.append(title if title is not None else ""						)
																								texts.append(__lowercase						)
						return {"title": titles, "text": texts}
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase						)       ->       dict:

						'''simple docstring'''



						_snake_case						:     List[Any]				=    ctx_tokenizer(
						    documents["title"]					,			documents["text"]					,			truncation=__lowercase					,			padding="longest"					,			return_tensors="pt"						)["input_ids"]
						_snake_case						:     Tuple				=    ctx_encoder(input_ids.to(device=__lowercase						)					,			return_dict=__lowercase						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			)       ->       str:

						'''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
						_snake_case						:     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
						_snake_case						:     Optional[int]				=    dataset.map(__lowercase					,			batched=__lowercase					,			num_proc=processing_args.num_proc						)

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

						# And finally save your dataset
						_snake_case						:     Dict				=    os.path.join(rag_example_args.output_dir					,			"my_knowledge_dataset"						)
						dataset.save_to_disk(__lowercase						)
						# 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
						_snake_case						:     Union[str, Any]				=    faiss.IndexHNSWFlat(index_hnsw_args.d					,			index_hnsw_args.m					,			faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index("embeddings"					,			custom_index=__lowercase						)

						# And save the index
						_snake_case						:     Any				=    os.path.join(rag_example_args.output_dir					,			"my_knowledge_dataset_hnsw_index.faiss"						)
						dataset.get_index("embeddings"						).save(__lowercase						)
						# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

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

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

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



@dataclass
class    lowercase_    :
							_lowerCamelCase						=		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\''}						, )
							_lowerCamelCase						=		field(
							    default=__snake_case						, 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=__snake_case						, metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						, )




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




@dataclass
class    lowercase_    :
							_lowerCamelCase						=		field(
							    default=768						, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						, )
							_lowerCamelCase						=		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						:    List[str]     = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
				__SCREAMING_SNAKE_CASE   ,__SCREAMING_SNAKE_CASE   ,__SCREAMING_SNAKE_CASE						:    int     = parser.parse_args_into_dataclasses()
				with TemporaryDirectory() as tmp_dir:
								__SCREAMING_SNAKE_CASE						:    int     = rag_example_args.output_dir or tmp_dir
								main(rag_example_args, processing_args, index_hnsw_args)