



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


a_						:     int	     =		logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_						:     List[str]	     =		'cuda' if torch.cuda.is_available() else 'cpu'

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=100   ,   UpperCAmelCase__=" " ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase								=		[], []
      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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   ):


      """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
      lowerCamelCase								=		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								=		dataset.map(UpperCAmelCase__   ,   batched=UpperCAmelCase__   ,   num_proc=processing_args.num_proc )

      # And compute the embeddings
      lowerCamelCase								=		DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=UpperCAmelCase__ )
      lowerCamelCase								=		DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
      lowerCamelCase								=		Features(
          {"text": Value("string" ), "title": Value("string" ), "embeddings": Sequence(Value("float32" ) )} )  # optional, save as float32 instead of float64 to save space
      lowerCamelCase								=		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								=		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								=		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								=		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    lowerCamelCase__  :
     """simple docstring"""



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





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			field(
         default=UpperCAmelCase_       ,    metadata={
             'help': 'The number of processes to use to split the documents into passages. Default is single process.'
         }       ,    )
     _A       =			field(
         default=16       ,    metadata={
             'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
         }       ,    )





@dataclass
class    lowerCamelCase__  :
     """simple docstring"""



     _A       =			field(
         default=7_68       ,    metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}       ,    )
     _A       =			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_						:     int	     =		HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
     a_     , a_     , a_						:     Dict	     =		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 os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a_						:     Tuple	     =		'src/transformers'
a_						:     Any	     =		'docs/source/en/tasks'

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      with open(UpperCAmelCase__   ,   "r"   ,   encoding="utf-8"   ,   newline="\n" ) as f:
            lowerCamelCase								=		f.readlines()
      # Find the start prompt.
      lowerCamelCase								=		0
      while not lines[start_index].startswith(UpperCAmelCase__ ):
            start_index += 1
      start_index += 1

      lowerCamelCase								=		start_index
      while not lines[end_index].startswith(UpperCAmelCase__ ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index] ) <= 1:
            start_index += 1
      while len(lines[end_index] ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
a_						:     Union[str, Any]	     =		direct_transformers_import(TRANSFORMERS_PATH)

a_						:     List[Any]	     =		{
    'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
a_						:     Union[str, Any]	     =		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		TASK_GUIDE_TO_MODELS[task_guide]
      lowerCamelCase								=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__   ,   set() )
      lowerCamelCase								=		{
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([F"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		_find_text_in_file(
          filename=os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"   ,   end_prompt="<!--End of the generated tip-->"   ,   )

      lowerCamelCase								=		get_model_list_for_task(UpperCAmelCase__ )

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(UpperCAmelCase__   ,   UpperCAmelCase__ )   ,   "w"   ,   encoding="utf-8"   ,   newline="\n" ) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
            else:
                  raise ValueError(
                      F"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
                      " to fix this." )


if __name__ == "__main__":
     a_						:     str	     =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_						:     str	     =		parser.parse_args()

     for task_guide in TASK_GUIDE_TO_MODELS.keys():
          check_model_list_for_task(task_guide, args.fix_and_overwrite)