





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


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


def _A				(   __magic_name__  , __magic_name__=100  , __magic_name__=" "	):
   lowercase__			       =  text.split(__magic_name__	)
   return [character.join(text[i : i + n]	).strip() for i in range(0  , len(__magic_name__	)  , __magic_name__	)]


def _A				(   __magic_name__	):
   lowercase__       ,							lowercase__			       =  [], []
   for title, text in zip(documents["title"]  , documents["text"]	):
      if text is not None:
         for passage in split_text(__magic_name__	):
            titles.append(title if title is not None else ""	)
            texts.append(__magic_name__	)
   return {"title": titles, "text": texts}


def _A				(   __magic_name__  , __magic_name__  , __magic_name__	):
   lowercase__			       =  ctx_tokenizer(
       documents["title"]  , documents["text"]  , truncation=__magic_name__  , padding="longest"  , return_tensors="pt"	)["input_ids"]
   lowercase__			       =  ctx_encoder(input_ids.to(device=__magic_name__	)  , return_dict=__magic_name__	).pooler_output
   return {"embeddings": embeddings.detach().cpu().numpy()}


def _A				(   __magic_name__  , __magic_name__  , __magic_name__  , ):
   ######################################
   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
   lowercase__			       =  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
   lowercase__			       =  dataset.map(__magic_name__  , batched=__magic_name__  , num_proc=processing_args.num_proc	)

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

   # And finally save your dataset
   lowercase__			       =  os.path.join(rag_example_args.output_dir  , "my_knowledge_dataset"	)
   dataset.save_to_disk(__magic_name__	)
   # 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
   lowercase__			       =  faiss.IndexHNSWFlat(index_hnsw_args.d  , index_hnsw_args.m  , faiss.METRIC_INNER_PRODUCT	)
   dataset.add_faiss_index("embeddings"  , custom_index=__magic_name__	)

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





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      default=str(Path(__lowercase ).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=__lowercase							,							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(__lowercase ).parent / 'test_run' / 'dummy-kb' )							,							metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}							,							)





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      default=__lowercase							,							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  lowerCAmelCase							:
  __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)

  _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)













import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)


def _A       (   __magic_name__	):
   lowercase__			       =  checkpoints.load_tax_checkpoint(__magic_name__	)
   lowercase__			       =  flatten_dict(__magic_name__	)
   return flax_params


def _A       (   __magic_name__	):
   lowercase__			       =  {}

   lowercase__			       =  {
       "token_embedder": "embeddings",
       "encoder_norm": "layernorm",
       "kernel": "weight",
       ".out": ".output",
       "scale": "weight",
       "embedders_0.pos_embedding": "row_embedder.weight",
       "embedders_1.pos_embedding": "column_embedder.weight",
   }

   lowercase__			       =  {
       "query": "attention.query",
       "key": "attention.key",
       "value": "attention.value",
       "output.dense": "output",
       "encoder_decoder_attention.o": "encoder_decoder_attention.attention.o",
       "pre_self_attention_layer_norm": "self_attention.layer_norm",
       "pre_cross_attention_layer_norm": "encoder_decoder_attention.layer_norm",
       "mlp.": "mlp.DenseReluDense.",
       "pre_mlp_layer_norm": "mlp.layer_norm",
       "self_attention.o": "self_attention.attention.o",
       "decoder.embeddings.embedding": "decoder.embed_tokens.weight",
       "decoder.relpos_bias.rel_embedding": "decoder.layer.0.self_attention.attention.relative_attention_bias.weight",
       "decoder.decoder_norm.weight": "decoder.final_layer_norm.weight",
       "decoder.logits_dense.weight": "decoder.lm_head.weight",
   }

   for key in flax_dict.keys():
      if "target" in key:
         # remove the first prefix from the key
         lowercase__			       =  ".".join(key[1:]	)

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            lowercase__			       =  new_key.replace(__magic_name__  , __magic_name__	)

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               lowercase__			       =  new_key.replace(__magic_name__  , __magic_name__	)

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            lowercase__			       =  re.sub(R"layers_(\d+)"  , R"layer.\1"  , __magic_name__	)
            lowercase__			       =  new_key.replace("encoder"  , "encoder.encoder"	)

         elif "layers" in new_key and "decoder" in new_key:
            # use regex to replace the layer number
            lowercase__			       =  re.sub(R"layers_(\d+)"  , R"layer.\1"  , __magic_name__	)

         lowercase__			       =  flax_dict[key]

   lowercase__			       =  {}
   # convert converted_dict into torch format
   for key in converted_dict.keys():
      if ("embed_tokens" not in key) and ("embedder" not in key):
         lowercase__			       =  torch.from_numpy(converted_dict[key].T	)
      else:
         lowercase__			       =  torch.from_numpy(converted_dict[key]	)

   return converted_torch_dict


def _A       (   __magic_name__  , __magic_name__  , __magic_name__=False  , __magic_name__=False	):
   lowercase__			       =  get_flax_param(__magic_name__	)

   if not use_large:
      lowercase__			       =  PixaStructVisionConfig()
      lowercase__			       =  PixaStructTextConfig()
   else:
      lowercase__			       =  PixaStructVisionConfig(
          hidden_size=1536  , d_ff=3968  , num_attention_heads=24  , num_hidden_layers=18	)
      lowercase__			       =  PixaStructTextConfig(hidden_size=1536  , d_ff=3968  , num_heads=24  , num_layers=18	)
   lowercase__			       =  PixaStructConfig(
       vision_config=encoder_config.to_dict()  , text_config=decoder_config.to_dict()  , is_vqa=__magic_name__	)

   lowercase__			       =  PixaStructForConditionalGeneration(__magic_name__	)

   lowercase__			       =  rename_and_convert_flax_params(__magic_name__	)
   model.load_state_dict(__magic_name__	)

   lowercase__			       =  AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"	)
   lowercase__			       =  PixaStructImageProcessor()
   lowercase__			       =  PixaStructProcessor(image_processor=__magic_name__  , tokenizer=__magic_name__	)

   if use_large:
      lowercase__			       =  4096

   lowercase__			       =  True

   # mkdir if needed
   os.makedirs(__magic_name__  , exist_ok=__magic_name__	)

   model.save_pretrained(__magic_name__	)
   processor.save_pretrained(__magic_name__	)

   print("Model saved in {}".format(__magic_name__	)	)


if __name__ == "__main__":
  _snake_case           =			argparse.ArgumentParser()
  parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
  parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
  parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
  _snake_case           =			parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )






