





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



def 							lowerCAmelCase_				(    A_  ,A_=1_00  ,A_=" "):
    UpperCamelCase__:					Any												=      text.split(A_)
    return [character.join(text[i : i + n]).strip() for i in range(0  ,len(A_)  ,A_)]



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__							,				UpperCamelCase__:					Optional[Any]												=      [], []
    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 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Tuple												=      ctx_tokenizer(
        documents["title"]  ,documents["text"]  ,truncation=A_  ,padding="longest"  ,return_tensors="pt")["input_ids"]
    UpperCamelCase__:					Optional[int]												=      ctx_encoder(input_ids.to(device=A_)  ,return_dict=A_).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}



def 							lowerCAmelCase_				(    A_  ,A_  ,A_  ,):
    ######################################
    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
    UpperCamelCase__:					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
    UpperCamelCase__:					List[Any]												=      dataset.map(A_  ,batched=A_  ,num_proc=processing_args.num_proc)

    # And compute the embeddings
    UpperCamelCase__:					Any												=      DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name).to(device=A_)
    UpperCamelCase__:					Union[str, Any]												=      DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name)
    UpperCamelCase__:					List[Any]												=      Features(
        {"text": Value("string"), "title": Value("string"), "embeddings": Sequence(Value("float32"))})  # optional, save as float32 instead of float64 to save space
    UpperCamelCase__:					Union[str, Any]												=      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
    UpperCamelCase__:					Tuple												=      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
    UpperCamelCase__:					str												=      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
    UpperCamelCase__:					Union[str, Any]												=      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
    UpperCamelCase__:					Tuple												=      RagRetriever.from_pretrained(
        rag_example_args.rag_model_name  ,index_name="custom"  ,indexed_dataset=A_)
    UpperCamelCase__:					str												=      RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,retriever=A_)
    UpperCamelCase__:					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")
    ######################################

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




@dataclass
class    _a  :

      """simple docstring"""


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




@dataclass
class    _a  :

      """simple docstring"""


      UpperCamelCase__       						=   field(
          default=lowercase_		,	metadata={
              """help""": """The number of processes to use to split the documents into passages. Default is single process."""
          }		,	)
      UpperCamelCase__       						=   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"""


      UpperCamelCase__       						=   field(
          default=768		,	metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}		,	)
      UpperCamelCase__       						=   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)

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








import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 							lowerCAmelCase_				(    ):
    UpperCamelCase__:					Optional[Any]												=      set()
    # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
    for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
        UpperCamelCase__:					Tuple												=      getattr(transformers.models.auto  ,A_  ,A_)
        if module is None:
            continue
        # all mappings in a single auto modeling file
        UpperCamelCase__:					Union[str, Any]												=      [
            x
            for x in dir(A_)
            if x.endswith("_MAPPING_NAMES")
            and (x.startswith("MODEL_") or x.startswith("TF_MODEL_") or x.startswith("FLAX_MODEL_"))
        ]
        for name in mapping_names:
            UpperCamelCase__:					Union[str, Any]												=      getattr(A_  ,A_)
            if mapping is not None:
                for v in mapping.values():
                    if isinstance(A_  ,(list, tuple)):
                        model_names.update(A_)
                    elif isinstance(A_  ,A_):
                        model_names.add(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    ):
    # All model names defined in auto mappings
    UpperCamelCase__:					Any												=      set(get_all_model_names())

    with open("tests/utils/tiny_model_summary.json") as fp:
        UpperCamelCase__:					Union[str, Any]												=      json.load(A_)
    UpperCamelCase__:					Union[str, Any]												=      set()
    for model_base_name in tiny_model_info:
        tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"])

    # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
    UpperCamelCase__:					Any												=      model_names.difference(A_)
    for model_name in copy.copy(A_):
        if not model_name.startswith("TF") and F"TF{model_name}" in not_on_hub:
            tiny_models_names.remove(A_)
        elif model_name.startswith("TF") and model_name[2:] in not_on_hub:
            tiny_models_names.remove(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      COMPOSITE_MODELS.values()

    # All tiny model base names on Hub
    UpperCamelCase__:					Any												=      get_all_model_names()
    UpperCamelCase__:					Tuple												=      hf_api.list_models(
        filter=ModelFilter(
            author="hf-internal-testing"  ,))
    UpperCamelCase__:					Optional[Any]												=      set()
    for x in models:
        UpperCamelCase__:					List[str]												=      x.modelId
        UpperCamelCase__							,				UpperCamelCase__:					Any												=      model.split("/")
        if not model.startswith("tiny-random-"):
            continue
        UpperCamelCase__:					Tuple												=      model.replace("tiny-random-"  ,"")
        if not model[0].isupper():
            continue
        if model not in model_names and model not in special_models:
            continue
        _models.add(A_)

    UpperCamelCase__:					List[Any]												=      sorted(_models)
    # All tiny model names on Hub
    UpperCamelCase__:					List[str]												=      {}
    for model in models:
        UpperCamelCase__:					str												=      F"hf-internal-testing/tiny-random-{model}"
        UpperCamelCase__:					Union[str, Any]												=      model.split("-")[0]
        try:
            UpperCamelCase__:					Any												=      hf_api.repo_info(A_)
            UpperCamelCase__:					int												=      {
                "tokenizer_classes": set(),
                "processor_classes": set(),
                "model_classes": set(),
                "sha": repo_info.sha,
            }
        except Exception:
            continue
        try:
            time.sleep(1)
            UpperCamelCase__:					Any												=      AutoTokenizer.from_pretrained(A_)
            content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      AutoTokenizer.from_pretrained(A_  ,use_fast=A_)
            content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      AutoImageProcessor.from_pretrained(A_)
            content["processor_classes"].add(img_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					Union[str, Any]												=      AutoFeatureExtractor.from_pretrained(A_)
            if not isinstance(A_  ,A_):
                content["processor_classes"].add(feat_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      getattr(A_  ,A_)
            UpperCamelCase__:					Union[str, Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      getattr(A_  ,F"TF{model}")
            UpperCamelCase__:					Optional[Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass

        UpperCamelCase__:					Optional[int]												=      sorted(content["tokenizer_classes"])
        UpperCamelCase__:					List[str]												=      sorted(content["processor_classes"])
        UpperCamelCase__:					Optional[int]												=      sorted(content["model_classes"])

        UpperCamelCase__:					int												=      content
        with open(os.path.join(A_  ,"hub_tiny_model_summary.json")  ,"w") as fp:
            json.dump(A_  ,A_  ,ensure_ascii=A_  ,indent=4)


if __name__ == "__main__":
   A__:  Optional[Any]       					=					argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A__:  Dict       					=					parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   A__:  List[Any]       					=					'''tiny_models'''
   A__:  Dict       					=					True
   A__:  Dict       					=					None
   A__:  Optional[Any]       					=					get_tiny_model_names_from_repo()
   A__:  int       					=					True
   A__:  int       					=					True
   A__:  int       					=					'''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )

