





"""simple docstring"""



import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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







def 		__lowercase     (   snake_case_  :  Optional[Any]				,snake_case_  :  List[str]				,snake_case_  :  Tuple						)   ->Dict:



    '''simple docstring'''







    __A    :						Dict						 =     os.path.abspath(snake_case_						)
    logger.info(F"""Converting TensorFlow checkpoint from {tf_path}"""						)
    # Load weights from TF model
    __A    :						Optional[Any]						 =     tf.train.list_variables(snake_case_						)
    __A    :						int						 =     []
    __A    :						Union[str, Any]						 =     []
    __A    :						str						 =     []
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        __A    :						Optional[int]						 =     full_name.split('''/'''						)
        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
            logger.info(F"""Skipping non-model layer {full_name}"""						)
            continue
        if "optimizer" in full_name:
            logger.info(F"""Skipping optimization layer {full_name}"""						)
            continue
        if name[0] == "model":
            # ignore initial 'model'
            __A    :						Union[str, Any]						 =     name[1:]
        # figure out how many levels deep the name is
        __A    :						Optional[int]						 =     0
        for _name in name:
            if _name.startswith('''layer_with_weights'''						):
                depth += 1
            else:
                break
        layer_depth.append(snake_case_						)
        # read data
        __A    :						Optional[Any]						 =     tf.train.load_variable(snake_case_				,snake_case_						)
        names.append('''/'''.join(snake_case_						)						)
        arrays.append(snake_case_						)
    logger.info(F"""Read a total of {len(snake_case_						):,} layers"""						)

    # Sanity check
    if len(set(snake_case_						)						) != 1:
        raise ValueError(F"""Found layer names with different depths (layer depth {list(set(snake_case_						)						)})"""						)
    __A    :						int						 =     list(set(snake_case_						)						)[0]
    if layer_depth != 1:
        raise ValueError(
            '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
            ''' heads.'''						)

    # convert layers
    logger.info('''Converting weights...'''						)
    for full_name, array in zip(snake_case_				,snake_case_						):
        __A    :						List[str]						 =     full_name.split('''/'''						)
        __A    :						List[Any]						 =     model
        __A    :						Tuple						 =     []
        for i, m_name in enumerate(snake_case_						):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('''layer_with_weights'''						):
                __A    :						int						 =     int(m_name.split('''-'''						)[-1]						)
                if layer_num <= 2:
                    # embedding layers
                    # layer_num 0: word_embeddings
                    # layer_num 1: position_embeddings
                    # layer_num 2: token_type_embeddings
                    continue
                elif layer_num == 3:
                    # embedding LayerNorm
                    trace.extend(['''embeddings''', '''LayerNorm''']						)
                    __A    :						Optional[int]						 =     getattr(snake_case_				,'''embeddings'''						)
                    __A    :						Any						 =     getattr(snake_case_				,'''LayerNorm'''						)
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['''encoder''', '''layer''', str(layer_num - 4						)]						)
                    __A    :						int						 =     getattr(snake_case_				,'''encoder'''						)
                    __A    :						Optional[Any]						 =     getattr(snake_case_				,'''layer'''						)
                    __A    :						Optional[Any]						 =     pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['''pooler''', '''dense''']						)
                    __A    :						int						 =     getattr(snake_case_				,'''pooler'''						)
                    __A    :						Any						 =     getattr(snake_case_				,'''dense'''						)
            elif m_name == "embeddings":
                trace.append('''embeddings'''						)
                __A    :						Any						 =     getattr(snake_case_				,'''embeddings'''						)
                if layer_num == 0:
                    trace.append('''word_embeddings'''						)
                    __A    :						Dict						 =     getattr(snake_case_				,'''word_embeddings'''						)
                elif layer_num == 1:
                    trace.append('''position_embeddings'''						)
                    __A    :						List[Any]						 =     getattr(snake_case_				,'''position_embeddings'''						)
                elif layer_num == 2:
                    trace.append('''token_type_embeddings'''						)
                    __A    :						Union[str, Any]						 =     getattr(snake_case_				,'''token_type_embeddings'''						)
                else:
                    raise ValueError(F"""Unknown embedding layer with name {full_name}"""						)
                trace.append('''weight'''						)
                __A    :						Any						 =     getattr(snake_case_				,'''weight'''						)
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['''attention''', '''self''']						)
                __A    :						List[Any]						 =     getattr(snake_case_				,'''attention'''						)
                __A    :						Dict						 =     getattr(snake_case_				,'''self'''						)
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['''attention''', '''output''', '''LayerNorm''']						)
                __A    :						List[str]						 =     getattr(snake_case_				,'''attention'''						)
                __A    :						List[str]						 =     getattr(snake_case_				,'''output'''						)
                __A    :						Tuple						 =     getattr(snake_case_				,'''LayerNorm'''						)
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['''attention''', '''output''', '''dense''']						)
                __A    :						int						 =     getattr(snake_case_				,'''attention'''						)
                __A    :						Union[str, Any]						 =     getattr(snake_case_				,'''output'''						)
                __A    :						List[str]						 =     getattr(snake_case_				,'''dense'''						)
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['''output''', '''dense''']						)
                __A    :						int						 =     getattr(snake_case_				,'''output'''						)
                __A    :						Dict						 =     getattr(snake_case_				,'''dense'''						)
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['''output''', '''LayerNorm''']						)
                __A    :						Union[str, Any]						 =     getattr(snake_case_				,'''output'''						)
                __A    :						Any						 =     getattr(snake_case_				,'''LayerNorm'''						)
            elif m_name == "_key_dense":
                # attention key
                trace.append('''key'''						)
                __A    :						str						 =     getattr(snake_case_				,'''key'''						)
            elif m_name == "_query_dense":
                # attention query
                trace.append('''query'''						)
                __A    :						Any						 =     getattr(snake_case_				,'''query'''						)
            elif m_name == "_value_dense":
                # attention value
                trace.append('''value'''						)
                __A    :						int						 =     getattr(snake_case_				,'''value'''						)
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['''intermediate''', '''dense''']						)
                __A    :						Union[str, Any]						 =     getattr(snake_case_				,'''intermediate'''						)
                __A    :						List[Any]						 =     getattr(snake_case_				,'''dense'''						)
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('''output'''						)
                __A    :						Optional[int]						 =     getattr(snake_case_				,'''output'''						)
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('''bias'''						)
                __A    :						Tuple						 =     getattr(snake_case_				,'''bias'''						)
            elif m_name in ["kernel", "gamma"]:
                trace.append('''weight'''						)
                __A    :						Dict						 =     getattr(snake_case_				,'''weight'''						)
            else:
                logger.warning(F"""Ignored {m_name}"""						)
        # for certain layers reshape is necessary
        __A    :						Dict						 =     '''.'''.join(snake_case_						)
        if re.match(r'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''				,snake_case_						) or re.match(
            r'''(\S+)\.attention\.output\.dense\.weight'''				,snake_case_						):
            __A    :						Any						 =     array.reshape(pointer.data.shape						)
        if "kernel" in full_name:
            __A    :						List[Any]						 =     array.transpose()
        if pointer.shape == array.shape:
            __A    :						str						 =     torch.from_numpy(snake_case_						)
        else:
            raise ValueError(
                F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
                F""" {array.shape}"""						)
        logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}"""						)
    return model







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  int				,snake_case_  :  Union[str, Any]						)   ->Optional[int]:



    '''simple docstring'''







    logger.info(F"""Loading model based on config from {config_path}..."""						)
    __A    :						Optional[Any]						 =     BertConfig.from_json_file(snake_case_						)
    __A    :						List[str]						 =     BertModel(snake_case_						)

    # Load weights from checkpoint
    logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}..."""						)
    load_tfa_weights_in_bert(snake_case_				,snake_case_				,snake_case_						)

    # Save pytorch-model
    logger.info(F"""Saving PyTorch model to {pytorch_dump_path}..."""						)
    torch.save(model.state_dict()				,snake_case_						)


if __name__ == "__main__":
       a_ =			argparse.ArgumentParser()
       parser.add_argument(
           """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
       )
       parser.add_argument(
           """--bert_config_file""",
           type=str,
           required=True,
           help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""",
           type=str,
           required=True,
           help="""Path to the output PyTorch model (must include filename).""",
       )
       a_ =			parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)











"""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 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Tuple=100				,snake_case_  :  int=" "						)   ->List[str]:



    '''simple docstring'''







    __A    :						Dict						 =     text.split(snake_case_						)
    return [character.join(text[i : i + n]						).strip() for i in range(0				,len(snake_case_						)				,snake_case_						)]







def 		__lowercase     (   snake_case_  :  dict						)   ->dict:



    '''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(snake_case_						):
                titles.append(title if title is not None else ''''''						)
                texts.append(snake_case_						)
    return {"title": titles, "text": texts}







def 		__lowercase     (   snake_case_  :  dict				,snake_case_  :  DPRContextEncoder				,snake_case_  :  DPRContextEncoderTokenizerFast						)   ->dict:



    '''simple docstring'''







    __A    :						Tuple						 =     ctx_tokenizer(
        documents['''title''']				,documents['''text''']				,truncation=snake_case_				,padding='''longest'''				,return_tensors='''pt'''						)['''input_ids''']
    __A    :						int						 =     ctx_encoder(input_ids.to(device=snake_case_						)				,return_dict=snake_case_						).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}







def 		__lowercase     (   snake_case_  :  "RagExampleArguments"				,snake_case_  :  "ProcessingArguments"				,snake_case_  :  "IndexHnswArguments"				,)   ->Tuple:



    '''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    :						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    :						str						 =     dataset.map(snake_case_				,batched=snake_case_				,num_proc=processing_args.num_proc						)

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

    # And finally save your dataset
    __A    :						int						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset'''						)
    dataset.save_to_disk(snake_case_						)
    # 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    :						Tuple						 =     faiss.IndexHNSWFlat(index_hnsw_args.d				,index_hnsw_args.m				,faiss.METRIC_INNER_PRODUCT						)
    dataset.add_faiss_index('''embeddings'''				,custom_index=snake_case_						)

    # 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(snake_case_						)
    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=str(Path(SCREAMING_SNAKE_CASE__     ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""     )							,							metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}							,							)
 _lowerCamelCase        =     field(
     default=SCREAMING_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=str(Path(SCREAMING_SNAKE_CASE__     ).parent / """test_run""" / """dummy-kb"""     )							,							metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}							,							)






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=SCREAMING_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 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=7_68							,							metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}							,							)
 _lowerCamelCase        =     field(
     default=1_28							,							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)




