
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,
)


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



def 						lowercase_			(   A__ ,		A__=100 ,		A__=" "		)							->							List[str]:
    """simple docstring"""

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



def 						lowercase_			(   A__		)							->							dict:
    """simple docstring"""

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



def 						lowercase_			(   A__ ,		A__ ,		A__		)							->							dict:
    """simple docstring"""

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



def 						lowercase_			(   A__ ,		A__ ,		A__ ,		)							->							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
    snake_case      =					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
    snake_case      =					dataset.map(A__ ,		batched=A__ ,		num_proc=processing_args.num_proc		)

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

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

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

    ######################################
    logger.info("Step 3 - Load RAG"		)
    ######################################

    # Easy way to load the model
    snake_case      =					RagRetriever.from_pretrained(
        rag_example_args.rag_model_name ,		index_name="custom" ,		indexed_dataset=A__		)
    snake_case      =					RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name ,		retriever=A__		)
    snake_case      =					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"		)
    ######################################

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







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







@dataclass
class      lowerCamelCase		:
     UpperCAmelCase__				:							Optional[int]           =		field(
         default=A_							, metadata={
             "help": "The number of processes to use to split the documents into passages. Default is single process."
         }							, )
     UpperCAmelCase__				:							int           =		field(
         default=16							, metadata={
             "help": "The batch size to use when computing the passages embeddings using the DPR context encoder."
         }							, )







@dataclass
class      lowerCamelCase		:
     UpperCAmelCase__				:							int           =		field(
         default=7_68							, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}							, )
     UpperCAmelCase__				:							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)

   _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)



import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch







class      lowerCamelCase		(  unittest.TestCase  ):





     @patch("sys.argv"				,    ["fakeprogrampath", "env"]   )
     def 		UpperCAmelCase(self      :    Optional[Any]   )						->						Dict:
         # test transformers-cli env
         import transformers.commands.transformers_cli

         with CaptureStd() as cs:
             transformers.commands.transformers_cli.main()
         self.assertIn("Python version"				,    cs.out   )
         self.assertIn("Platform"				,    cs.out   )
         self.assertIn("Using distributed or parallel set-up in script?"				,    cs.out   )





     @is_pt_tf_cross_test
     @patch(
         "sys.argv"				,    ["fakeprogrampath", "pt-to-tf", "--model-name", "hf-internal-testing/tiny-random-gptj", "--no-pr"]   )
     def 		UpperCAmelCase(self      :    Any   )						->						int:
         import transformers.commands.transformers_cli

         shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"				,    ignore_errors=_A   )  # cleans potential past runs
         transformers.commands.transformers_cli.main()

         # The original repo has no TF weights -- if they exist, they were created by the CLI
         self.assertTrue(os.path.exists("/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"   )   )





     @require_torch
     @patch("sys.argv"				,    ["fakeprogrampath", "download", "hf-internal-testing/tiny-random-gptj", "--cache-dir", "/tmp"]   )
     def 		UpperCAmelCase(self      :    Dict   )						->						List[Any]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--tiny-random-gptj"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/refs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"   )   )





     @require_torch
     @patch(
         "sys.argv"				,    [
             "fakeprogrampath",
             "download",
             "hf-internal-testing/test_dynamic_model_with_tokenizer",
             "--trust-remote-code",
             "--cache-dir",
             "/tmp",
         ]				,    )
     def 		UpperCAmelCase(self      :    Any   )						->						List[str]:
         import transformers.commands.transformers_cli

         # # remove any previously downloaded model to start clean
         shutil.rmtree("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"				,    ignore_errors=_A   )

         # run the command
         transformers.commands.transformers_cli.main()

         # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"   )   )
         self.assertTrue(os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"   )   )
         self.assertTrue(
             os.path.exists("/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"   )   )

