






"""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


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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Optional[int]=1_0_0,						UpperCAmelCase__       :					Tuple=" "      )		->List[str]:
						A__						:  Optional[Any]		  =  text.split(UpperCAmelCase__      )
						return [character.join(text[i : i + n]      ).strip() for i in range(0,						len(UpperCAmelCase__      ),						UpperCAmelCase__      )]




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




def      _lowerCAmelCase		(							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
						A__						:  Optional[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
						A__						:  Union[str, Any]		  =  dataset.map(UpperCAmelCase__,						batched=UpperCAmelCase__,						num_proc=processing_args.num_proc      )

						# And compute the embeddings
						A__						:  str		  =  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__						:  Union[str, Any]		  =  Features(
						    {"""text""": Value("""string"""      ), """title""": Value("""string"""      ), """embeddings""": Sequence(Value("""float32"""      )      )}      )  # optional, save as float32 instead of float64 to save space
						A__						:  List[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__						:  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
						A__						:  List[Any]		  =  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__						:  Optional[int]		  =  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 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							field(
							    default=str(Path(UpperCamelCase		).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'		)       ,		metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}       ,		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}       ,		)
							snake_case_     =							field(
							    default='facebook/rag-sequence-nq'       ,		metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}       ,		)
							snake_case_     =							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\''
							        )
							    }       ,		)
							snake_case_     =							field(
							    default=str(Path(UpperCamelCase		).parent / 'test_run' / 'dummy-kb'		)       ,		metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}       ,		)






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






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

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














"""simple docstring"""



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

A_    =							{
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
A_    =							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str]      )		->str:
						for attribute in key.split("""."""      ):
												A__						:  Any		  =  getattr(UpperCAmelCase__,						UpperCAmelCase__      )

						if weight_type is not None:
												A__						:  Union[str, Any]		  =  getattr(UpperCAmelCase__,						UpperCAmelCase__      ).shape
						else:
												A__						:  Optional[int]		  =  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":
												A__						:  List[Any]		  =  value
						elif weight_type == "weight_g":
												A__						:  Any		  =  value
						elif weight_type == "weight_v":
												A__						:  Optional[int]		  =  value
						elif weight_type == "bias":
												A__						:  Any		  =  value
						else:
												A__						:  Any		  =  value

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Any      )		->Union[str, Any]:
						A__						:  Dict		  =  []
						A__						:  int		  =  fairseq_model.state_dict()

						A__						:  Dict		  =  hf_model.feature_extractor
						A__						:  Any		  =  hf_model.adapter

						for name, value in fairseq_dict.items():
												A__						:  Optional[int]		  =  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						hf_model.config.feat_extract_norm == """group""",						)
																		A__						:  Union[str, Any]		  =  True
												elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
																		load_adapter(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
																		A__						:  int		  =  True
												else:
																		for key, mapped_key in MAPPING.items():
																								if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
																														A__						:  List[Any]		  =  True
																														if "*" in mapped_key:
																																				A__						:  Union[str, Any]		  =  name.split(UpperCAmelCase__      )[0].split("""."""      )[-2]
																																				A__						:  Union[str, Any]		  =  mapped_key.replace("""*""",						UpperCAmelCase__      )
																														if "weight_g" in name:
																																				A__						:  List[str]		  =  """weight_g"""
																														elif "weight_v" in name:
																																				A__						:  Optional[Any]		  =  """weight_v"""
																														elif "bias" in name:
																																				A__						:  Optional[int]		  =  """bias"""
																														elif "weight" in name:
																																				A__						:  int		  =  """weight"""
																														else:
																																				A__						:  Optional[Any]		  =  None
																														set_recursively(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
																								continue
												if not is_used:
																		unused_weights.append(UpperCAmelCase__      )

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Any      )		->List[Any]:
						A__						:  Dict		  =  full_name.split("""conv_layers."""      )[-1]
						A__						:  Optional[int]		  =  name.split("""."""      )
						A__						:  int		  =  int(items[0]      )
						A__						:  Tuple		  =  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.'
																		)
																		A__						:  Optional[Any]		  =  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.'
																		)
																		A__						:  Optional[int]		  =  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."
																		)
																		A__						:  Optional[int]		  =  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.'
																		)
																		A__						:  Any		  =  value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
						else:
												unused_weights.append(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Dict      )		->str:
						A__						:  Tuple		  =  full_name.split("""adaptor."""      )[-1]
						A__						:  Optional[int]		  =  name.split("""."""      )

						if items[1].isdigit():
												A__						:  Optional[Any]		  =  int(items[1]      )
						else:
												A__						:  Any		  =  None

						if "adaptor" not in full_name:
												if "proj_ln" in full_name:
																		# has to be layer norm
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.bias.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																								A__						:  Union[str, Any]		  =  value
																								logger.info(f'Adapter proj layer norm bias was initialized from {full_name}.'      )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj_layer_norm.weight.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																								A__						:  Tuple		  =  value
												else:
																		# has to be projection layer
																		if "bias" in name:
																								assert (
																								    value.shape == adapter.proj.bias.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																								A__						:  str		  =  value
																								logger.info(f'Adapter proj layer bias was initialized from {full_name}.'      )
																		if "weight" in name:
																								assert (
																								    value.shape == adapter.proj.weight.data.shape
																								), f'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																								A__						:  Union[str, Any]		  =  value
																								logger.info(f'Adapter proj layer weight was initialized from {full_name}.'      )
						elif isinstance(UpperCAmelCase__,						UpperCAmelCase__      ):
												if "bias" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.bias.data.shape
																		), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
																		A__						:  Dict		  =  value
																		logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'      )
												elif "weight" in name:
																		assert (
																		    value.shape == adapter.layers[layer_id].conv.weight.data.shape
																		), f'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
																		A__						:  Optional[int]		  =  value
																		logger.info(f'Adapter layer {layer_id} bias was initialized from {full_name}.'      )
						else:
												unused_weights.append(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any]      )		->List[Any]:
						A__  ,  A__						:  Union[str, Any]		  =  emb.weight.shape
						A__						:  List[str]		  =  nn.Linear(UpperCAmelCase__,						UpperCAmelCase__,						bias=UpperCAmelCase__      )
						A__						:  List[Any]		  =  emb.weight.data
						return lin_layer




@torch.no_grad()
def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Dict,						)		->str:
						A__						:  Tuple		  =  WavaVecaConfig.from_pretrained(
						    UpperCAmelCase__,						add_adapter=UpperCAmelCase__,						adapter_stride=UpperCAmelCase__,						adapter_kernel_size=UpperCAmelCase__,						use_auth_token=UpperCAmelCase__,						output_hidden_size=UpperCAmelCase__,						)
						A__						:  List[Any]		  =  MBartConfig.from_pretrained(UpperCAmelCase__      )

						# load model
						A__  ,  A__  ,  A__						:  str		  =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path],						arg_overrides={
						        """config_yaml""": config_yaml_path,
						        """data""": """/""".join(dict_path.split("""/"""      )[:-1]      ),
						        """w2v_path""": checkpoint_path,
						        """load_pretrained_decoder_from""": None,
						    },						)
						A__						:  List[Any]		  =  model[0].eval()

						# load feature extractor
						A__						:  Any		  =  WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase__,						use_auth_token=UpperCAmelCase__      )

						# set weights for wav2vec2 encoder
						A__						:  Dict		  =  WavaVecaModel(UpperCAmelCase__      )

						recursively_load_weights_wavaveca(model.encoder,						UpperCAmelCase__      )

						# load decoder weights
						A__						:  Any		  =  MBartForCausalLM(UpperCAmelCase__      )
						A__  ,  A__						:  Union[str, Any]		  =  hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),						strict=UpperCAmelCase__      )
						logger.warning(f'The following keys are missing when loading the decoder weights: {missing_keys}'      )
						logger.warning(f'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'      )

						A__						:  Dict		  =  SpeechEncoderDecoderModel(encoder=UpperCAmelCase__,						decoder=UpperCAmelCase__      )
						A__						:  Optional[Any]		  =  False

						A__						:  Optional[Any]		  =  MBartaaTokenizer(UpperCAmelCase__      )
						tokenizer.save_pretrained(UpperCAmelCase__      )

						A__						:  Dict		  =  hf_wavavec.config.to_dict()
						A__						:  List[Any]		  =  tokenizer.pad_token_id
						A__						:  Optional[Any]		  =  tokenizer.bos_token_id
						A__						:  List[Any]		  =  tokenizer.eos_token_id
						A__						:  Tuple		  =  """mbart50"""
						A__						:  List[str]		  =  """wav2vec2"""

						A__						:  Optional[int]		  =  tokenizer.eos_token_id
						A__						:  int		  =  2_5_0_0_0_4
						A__						:  Dict		  =  tokenizer.eos_token_id

						A__						:  str		  =  SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase__      )

						hf_wavavec.save_pretrained(UpperCAmelCase__      )
						feature_extractor.save_pretrained(UpperCAmelCase__      )


if __name__ == "__main__":
						A_    =							argparse.ArgumentParser()
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
						parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
						parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
						parser.add_argument(
						    '''--encoder_config_path''',
						    default='''facebook/wav2vec2-xls-r-1b''',
						    type=str,
						    help='''Path to hf encoder wav2vec2 checkpoint config''',
						)
						parser.add_argument(
						    '''--decoder_config_path''',
						    default='''facebook/mbart-large-50-one-to-many-mmt''',
						    type=str,
						    help='''Path to hf decoder checkpoint config''',
						)
						parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
						parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
						parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
						parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
						parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

						A_    =							parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path,
						    args.pytorch_dump_folder_path,
						    args.dict_path,
						    args.config_yaml_path,
						    encoder_config_path=args.encoder_config_path,
						    decoder_config_path=args.decoder_config_path,
						    add_adapter=args.add_adapter,
						    adapter_kernel_size=args.adapter_kernel_size,
						    adapter_stride=args.adapter_stride,
						    decoder_start_token_id=args.start_token_id,
						    encoder_output_dim=args.encoder_output_dim,
						)






