


"""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


__lowerCamelCase      :Optional[Any]	    =		logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowerCamelCase      :List[Any]	    =		'cuda' if torch.cuda.is_available() else 'cpu'




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						Dict=100			,				UpperCamelCase__       :						int=" "    )   ->      List[str]:
      lowerCamelCase					:					int        =	text.split(UpperCamelCase__    )
      return [character.join(text[i : i + n]    ).strip() for i in range(0			,				len(UpperCamelCase__    )			,				UpperCamelCase__    )]




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




def   snake_case		(     UpperCamelCase__       :						"RagExampleArguments"			,				UpperCamelCase__       :						"ProcessingArguments"			,				UpperCamelCase__       :						"IndexHnswArguments"			,				)   ->      str:
      ######################################
      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
      lowerCamelCase					:					Union[str, 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
      lowerCamelCase					:					List[Any]        =	dataset.map(UpperCamelCase__			,				batched=UpperCamelCase__			,				num_proc=processing_args.num_proc    )

      # And compute the embeddings
      lowerCamelCase					:					Any        =	DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    ).to(device=UpperCamelCase__    )
      lowerCamelCase					:					List[str]        =	DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    )
      lowerCamelCase					:					List[str]        =	Features(
          {"""text""": Value("""string"""    ), """title""": Value("""string"""    ), """embeddings""": Sequence(Value("""float32"""    )    )}    )  # optional, save as float32 instead of float64 to save space
      lowerCamelCase					:					List[Any]        =	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
      lowerCamelCase					:					Tuple        =	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
      lowerCamelCase					:					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
      lowerCamelCase					:					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 			A__	:


     """simple docstring"""
     snake_case__	:					str													=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\''''} ,     )
     snake_case__	:					Optional[str]													=field(
         default=__lowercase ,     metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''} ,     )
     snake_case__	:					str													=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__	:					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\''''
             )
         } ,     )
     snake_case__	:					Optional[str]													=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 			A__	:


     """simple docstring"""
     snake_case__	:					Optional[int]													=field(
         default=__lowercase ,     metadata={
             '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
         } ,     )
     snake_case__	:					int													=field(
         default=16 ,     metadata={
             '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
         } ,     )


@dataclass
class 			A__	:


     """simple docstring"""
     snake_case__	:					int													=field(
         default=7_68 ,     metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''} ,     )
     snake_case__	:					int													=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)

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








"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Tuple     ,		__a:						Optional[int]	)->    List[str]:
           return None


class 			A__	:


     """simple docstring"""

     def 		a__						(	self:						Optional[int]     ,		__a:						Tuple     ,		__a:						str     ,		__a:						str     ,		__a:						str	)->    Tuple:
           return None


class 			A__	(						unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Optional[Any]													=[
         # (model_name, model_kwargs)
         ('''bert-base-cased''', {}),
         ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
     ]

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						str	)->    int:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)

     @require_torch
     @slow
     def 		a__						(	self:						Union[str, Any]	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
           with NamedTemporaryFile(mode="""w+t"""	) as vocab_file:
                 vocab_file.write("""\n""".join(__a	)	)
                 vocab_file.flush()
                 lowerCamelCase					:					Dict        =	BertTokenizerFast(vocab_file.name	)

           with TemporaryDirectory() as bert_save_dir:
                 lowerCamelCase					:					List[str]        =	BertModel(BertConfig(vocab_size=len(__a	)	)	)
                 model.save_pretrained(__a	)
                 self._test_export(__a     ,		"""pt"""     ,		12     ,		__a	)

     @require_tf
     @slow
     def 		a__						(	self:						Optional[Any]	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Optional[int]        =	self._test_export(__a     ,		"""tf"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Tuple        =	quantize(Path(__a	)	)

                 # Ensure the actual quantized model is not bigger than the original one
                 if quantized_path.stat().st_size >= Path(__a	).stat().st_size:
                       self.fail("""Quantized model is bigger than initial ONNX model"""	)

     @require_torch
     @slow
     def 		a__						(	self:						Any	)->    Optional[int]:
           for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
                 lowerCamelCase					:					Any        =	self._test_export(__a     ,		"""pt"""     ,		12     ,		**__a	)
                 lowerCamelCase					:					Dict        =	quantize(__a	)

                 # Ensure the actual quantized model is not bigger than the original one
                 if quantized_path.stat().st_size >= Path(__a	).stat().st_size:
                       self.fail("""Quantized model is bigger than initial ONNX model"""	)

     def 		a__						(	self:						List[Any]     ,		__a:						Optional[Any]     ,		__a:						List[Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]=None     ,		**__a:						Optional[int]	)->    Any:
           try:
                 # Compute path
                 with TemporaryDirectory() as tempdir:
                       lowerCamelCase					:					Optional[Any]        =	Path(__a	).joinpath("""model.onnx"""	)

                 # Remove folder if exists
                 if path.parent.exists():
                       path.parent.rmdir()

                 # Export
                 convert(__a     ,		__a     ,		__a     ,		__a     ,		__a     ,		**__a	)

                 return path
           except Exception as e:
                 self.fail(__a	)

     @require_torch
     @require_tokenizers
     @slow
     def 		a__						(	self:						Tuple	)->    Dict:
           from transformers import BertModel

           lowerCamelCase					:					int        =	BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
           lowerCamelCase					:					List[Any]        =	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
           self._test_infer_dynamic_axis(__a     ,		__a     ,		"""pt"""	)

     @require_tf
     @require_tokenizers
     @slow
     def 		a__						(	self:						Optional[Any]	)->    List[Any]:
           from transformers import TFBertModel

           lowerCamelCase					:					Union[str, Any]        =	TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random"""	)	)
           lowerCamelCase					:					str        =	BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random"""	)
           self._test_infer_dynamic_axis(__a     ,		__a     ,		"""tf"""	)

     def 		a__						(	self:						List[str]     ,		__a:						str     ,		__a:						Optional[Any]     ,		__a:						str	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	FeatureExtractionPipeline(__a     ,		__a	)

           lowerCamelCase					:					List[str]        =	["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	infer_shapes(__a     ,		__a	)

           # Assert all variables are present
           self.assertEqual(len(__a	)     ,		len(__a	)	)
           self.assertTrue(all(var_name in shapes for var_name in variable_names	)	)
           self.assertSequenceEqual(variable_names[:3]     ,		__a	)
           self.assertSequenceEqual(variable_names[3:]     ,		__a	)

           # Assert inputs are {0: batch, 1: sequence}
           for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
                 self.assertDictEqual(shapes[var_name]     ,		{0: """batch""", 1: """sequence"""}	)

           # Assert outputs are {0: batch, 1: sequence} and {0: batch}
           self.assertDictEqual(shapes["""output_0"""]     ,		{0: """batch""", 1: """sequence"""}	)
           self.assertDictEqual(shapes["""output_1"""]     ,		{0: """batch"""}	)

     def 		a__						(	self:						List[Any]	)->    int:
           lowerCamelCase					:					List[str]        =	["""input_ids""", """attention_mask""", """token_type_ids"""]
           lowerCamelCase					:					str        =	{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncContiguousArgs()     ,		__a     ,		__a	)

           # Should have exactly the same number of args (all are valid)
           self.assertEqual(len(__a	)     ,		3	)

           # Should have exactly the same input names
           self.assertEqual(set(__a	)     ,		set(__a	)	)

           # Parameter should be reordered according to their respective place in the function:
           # (input_ids, token_type_ids, attention_mask)
           self.assertEqual(__a     ,		(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""])	)

           # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	ensure_valid_input(FuncNonContiguousArgs()     ,		__a     ,		__a	)

           # Should have exactly the one arg (all before the one not provided "some_other_args")
           self.assertEqual(len(__a	)     ,		1	)
           self.assertEqual(len(__a	)     ,		1	)

           # Should have only "input_ids"
           self.assertEqual(inputs_args[0]     ,		tokens["""input_ids"""]	)
           self.assertEqual(ordered_input_names[0]     ,		"""input_ids"""	)







     def 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					Optional[int]        =	generate_identified_filename(Path("""/home/something/my_fake_model.onnx"""	)     ,		"""-test"""	)
           self.assertEqual("""/home/something/my_fake_model-test.onnx"""     ,		generated.as_posix()	)




