




'''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,
)


lowerCAmelCase_			:     Tuple							    =     logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCAmelCase_			:     Optional[int]							    =     'cuda' if torch.cuda.is_available() else 'cpu'



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Optional[Any]=100   ,						UpperCamelCase__						:				int=" "							):




     """simple docstring"""


     a_    :       Union[str, Any]    	=     text.split(_UpperCAmelCase							)
     return [character.join(text[i : i + n]							).strip() for i in range(0   ,						len(_UpperCAmelCase							)   ,						_UpperCAmelCase							)]



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]							):




     """simple docstring"""


     a_	,    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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str   ,						UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Optional[int]							):




     """simple docstring"""


     a_    :       Dict    	=     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    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Optional[Any]   ,						UpperCamelCase__						:				Union[str, Any]   ,						):




     """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_    :       List[Any]    	=     dataset.map(_UpperCAmelCase   ,						batched=_UpperCAmelCase   ,						num_proc=processing_args.num_proc							)

     # And compute the embeddings
     a_    :       Tuple    	=     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name							).to(device=_UpperCAmelCase							)
     a_    :       int    	=     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_    :       int    	=     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_    :       Union[str, 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_    :       Optional[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_    :       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_    :       Union[str, Any]    	=     RagRetriever.from_pretrained(
         rag_example_args.rag_model_name   ,						index_name="""custom"""   ,						indexed_dataset=_UpperCAmelCase							)
     a_    :       Dict    	=     RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name   ,						retriever=_UpperCAmelCase							)
     a_    :       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"""							)
     ######################################

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







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







@dataclass
class     SCREAMING_SNAKE_CASE						:
      __magic_name__     :					str             =				field(
          default=lowerCamelCase__       ,			metadata={
              '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
          }       ,			)
      __magic_name__     :					Optional[Any]             =				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						:
      __magic_name__     :					Dict             =				field(
          default=768       ,			metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}       ,			)
      __magic_name__     :					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)

 lowerCAmelCase_			:     Tuple							    =     HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
 lowerCAmelCase_    ,      lowerCAmelCase_    ,      lowerCAmelCase_			:     Union[str, Any]							    =     parser.parse_args_into_dataclasses()
 with TemporaryDirectory() as tmp_dir:
  lowerCAmelCase_			:     Dict							    =     rag_example_args.output_dir or tmp_dir
  main(rag_example_args, processing_args, index_hnsw_args)






'''simple docstring'''


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				Union[str, Any]   ,						UpperCamelCase__						:				List[Any]   ,						UpperCamelCase__						:				Dict							):




     """simple docstring"""


     a_    :       Optional[int]    	=     TaConfig.from_pretrained(UpperCamelCase__							)
     a_    :       List[str]    	=     FlaxTaForConditionalGeneration(config=UpperCamelCase__							)
     a_    :       List[Any]    	=     checkpoints.load_tax_checkpoint(UpperCamelCase__							)

     a_    :       List[str]    	=     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
          a_    :       Optional[Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

          if split_mlp_wi:
               a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       str    	=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Union[str, Any]    	=     tax_attention_key
          a_    :       Dict    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       List[str]    	=     tax_attention_value

          a_    :       Union[str, Any]    	=     tax_attention_layer_norm

          if split_mlp_wi:
               a_    :       Tuple    	=     tax_mlp_wi_a
               a_    :       Dict    	=     tax_mlp_wi_a
          else:
               a_    :       Optional[Any]    	=     tax_mlp_wi

          a_    :       Optional[int]    	=     tax_mlp_wo
          a_    :       Tuple    	=     tax_mlp_layer_norm

     # Only for layer 0:
     a_    :       Tuple    	=     tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_    :       Union[str, Any]    	=     tax_encoder_rel_embedding

     # Assigning
     a_    :       Dict    	=     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_    :       Optional[Any]    	=     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers							):
          a_    :       int    	=     F"layers_{str(UpperCamelCase__							)}"

          # Self-Attention
          a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
          a_    :       int    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
          a_    :       Any    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
              """scale"""
          ]

          # Encoder-Decoder-Attention
          a_    :       Union[str, Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
              """kernel"""
          ]
          a_    :       str    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
              """kernel"""
          ]
          a_    :       List[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
              """kernel"""
          ]

          # Layer Normalization
          a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

          # MLP
          if split_mlp_wi:
               a_    :       Tuple    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_    :       Optional[int]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_    :       Dict    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_    :       Optional[Any]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_    :       List[str]    	=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_    :       Any    	=     tax_attention_key
          a_    :       Any    	=     tax_attention_out
          a_    :       Any    	=     tax_attention_query
          a_    :       Union[str, Any]    	=     tax_attention_value

          a_    :       Any    	=     tax_pre_attention_layer_norm

          a_    :       Optional[int]    	=     tax_enc_dec_attention_key
          a_    :       List[Any]    	=     tax_enc_dec_attention_out
          a_    :       Any    	=     tax_enc_dec_attention_query
          a_    :       Tuple    	=     tax_enc_dec_attention_value

          a_    :       List[str]    	=     tax_cross_layer_norm

          if split_mlp_wi:
               a_    :       int    	=     tax_mlp_wi_a
               a_    :       Optional[int]    	=     tax_mlp_wi_a
          else:
               a_    :       Tuple    	=     tax_mlp_wi

          a_    :       List[Any]    	=     tax_mlp_wo

          a_    :       Union[str, Any]    	=     txa_mlp_layer_norm

     # Decoder Normalization
     a_    :       str    	=     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_    :       Dict    	=     txa_decoder_norm

     # Only for layer 0:
     a_    :       Dict    	=     tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_    :       Any    	=     tax_decoder_rel_embedding

     # Token Embeddings
     a_    :       Union[str, Any]    	=     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_    :       Optional[Any]    	=     txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          a_    :       int    	=     tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

     flax_model.save_pretrained(UpperCamelCase__							)
     print("""T5X Model was sucessfully converted!"""							)


if __name__ == "__main__":
 lowerCAmelCase_			:     List[Any]							    =     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
 )
 parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
 parser.add_argument(
     '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
 )
 lowerCAmelCase_			:     str							    =     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
