






import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
       import torch

       from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin
class     a    :



      """simple docstring"""


      def __init__(      self					:			int		,      __lowercase					:			str		,      __lowercase					:			Union[str, Any]=None		,      __lowercase					:			int=None		,      __lowercase					:			Union[str, Any]=None		,      __lowercase					:			Optional[int]="resnet50"		,      __lowercase					:			Union[str, Any]=3		,      __lowercase					:			Any=32		,      __lowercase					:			Optional[int]=3		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Dict=True		,      )							->      List[str]:
        __UpperCAmelCase							:	Union[str, Any]           =  parent
        __UpperCAmelCase							:	Tuple           =  out_indices if out_indices is not None else [4]
        __UpperCAmelCase							:	Tuple           =  stage_names
        __UpperCAmelCase							:	Dict           =  out_features
        __UpperCAmelCase							:	int           =  backbone
        __UpperCAmelCase							:	Dict           =  batch_size
        __UpperCAmelCase							:	List[Any]           =  image_size
        __UpperCAmelCase							:	Dict           =  num_channels
        __UpperCAmelCase							:	str           =  use_pretrained_backbone
        __UpperCAmelCase							:	List[Any]           =  is_training
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[Any]:
        __UpperCAmelCase							:	Tuple           =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
        __UpperCAmelCase							:	Dict           =  self.get_config()

        return config, pixel_values
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Optional[Any]:
        return TimmBackboneConfig(
            image_size=self.image_size		,      num_channels=self.num_channels		,      out_features=self.out_features		,      out_indices=self.out_indices		,      stage_names=self.stage_names		,      use_pretrained_backbone=self.use_pretrained_backbone		,      backbone=self.backbone		,      )
      def 	UpperCAmelCase      (      self					:			str		,      __lowercase					:			List[str]		,      __lowercase					:			str					)							->      List[str]:
        __UpperCAmelCase							:	Union[str, Any]           =  TimmBackbone(config=__lowerCamelCase					)
        model.to(__lowerCamelCase					)
        model.eval()
        with torch.no_grad():
          __UpperCAmelCase							:	Tuple           =  model(__lowerCamelCase					)
        self.parent.assertEqual(
            result.feature_map[-1].shape		,      (self.batch_size, model.channels[-1], 14, 14)		,      )




      def 	UpperCAmelCase      (      self					:			int					)							->      Optional[Any]:
        __UpperCAmelCase							:	int           =  self.prepare_config_and_inputs()
        __UpperCAmelCase							:	List[Any]           =  config_and_inputs
        __UpperCAmelCase							:	Union[str, Any]           =  {'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_torch
@require_timm
class     a    ( SCREAMING_SNAKE_CASE__							,       SCREAMING_SNAKE_CASE__							,       SCREAMING_SNAKE_CASE__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : Dict							       =	(TimmBackbone,) if is_torch_available() else ()
      a  : Tuple							       =	{'feature-extraction': TimmBackbone} if is_torch_available() else {}
      a  : str							       =	False
      a  : Optional[Any]							       =	False
      a  : Union[str, Any]							       =	False
      a  : Dict							       =	False
      def 	UpperCAmelCase      (      self					:			int					)							->      Union[str, Any]:
        __UpperCAmelCase							:	Any           =  TimmBackboneModelTester(self					)
        __UpperCAmelCase							:	Optional[int]           =  ConfigTester(self		,      config_class=__lowerCamelCase		,      has_text_modality=__lowerCamelCase					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Any:
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()
      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Tuple:
        __UpperCAmelCase							:	Union[str, Any]           =  '''resnet18'''
        __UpperCAmelCase							:	Tuple           =  '''microsoft/resnet-18'''

        __UpperCAmelCase							:	Tuple           =  AutoBackbone.from_pretrained(__lowerCamelCase		,      use_timm_backbone=__lowerCamelCase					)
        __UpperCAmelCase							:	List[str]           =  AutoBackbone.from_pretrained(__lowerCamelCase					)

        self.assertEqual(len(timm_model.out_features					)		,      len(transformers_model.out_features					)					)
        self.assertEqual(len(timm_model.stage_names					)		,      len(transformers_model.stage_names					)					)
        self.assertEqual(timm_model.channels		,      transformers_model.channels					)
        # Out indices are set to the last layer by default. For timm models, we don't know
        # the number of layers in advance, so we set it to (-1,), whereas for transformers
        # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
        self.assertEqual(timm_model.out_indices		,      (-1,)					)
        self.assertEqual(transformers_model.out_indices		,      [len(timm_model.stage_names					) - 1]					)

        __UpperCAmelCase							:	Optional[int]           =  AutoBackbone.from_pretrained(__lowerCamelCase		,      use_timm_backbone=__lowerCamelCase		,      out_indices=[1, 2, 3]					)
        __UpperCAmelCase							:	Dict           =  AutoBackbone.from_pretrained(__lowerCamelCase		,      out_indices=[1, 2, 3]					)

        self.assertEqual(timm_model.out_indices		,      transformers_model.out_indices					)
        self.assertEqual(len(timm_model.out_features					)		,      len(transformers_model.out_features					)					)
        self.assertEqual(timm_model.channels		,      transformers_model.channels					)
      @unittest.skip("""TimmBackbone doesn\'t support feed forward chunking"""					)
      def 	UpperCAmelCase      (      self					:			str					)							->      List[str]:
        pass
      @unittest.skip("""TimmBackbone doesn\'t have num_hidden_layers attribute"""					)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[Any]:
        pass
      @unittest.skip("""TimmBackbone initialization is managed on the timm side"""					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Tuple:
        pass
      @unittest.skip("""TimmBackbone models doesn\'t have inputs_embeds"""					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[str]:
        pass
      @unittest.skip("""TimmBackbone models doesn\'t have inputs_embeds"""					)
      def 	UpperCAmelCase      (      self					:			int					)							->      Any:
        pass
      @unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Dict:
        pass
      @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      int:
        pass
      @unittest.skip("""model weights aren\'t tied in TimmBackbone."""					)
      def 	UpperCAmelCase      (      self					:			int					)							->      Dict:
        pass
      @unittest.skip("""model weights aren\'t tied in TimmBackbone."""					)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[int]:
        pass
      @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      List[str]:
        pass
      @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""					)
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Optional[Any]:
        pass
      @unittest.skip("""TimmBackbone doesn\'t have hidden size info in its configuration."""					)
      def 	UpperCAmelCase      (      self					:			str					)							->      Union[str, Any]:
        pass
      @unittest.skip("""TimmBackbone doesn\'t support output_attentions."""					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      str:
        pass
      @unittest.skip("""Safetensors is not supported by timm."""					)
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      Optional[Any]:
        pass
      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Dict:
        pass
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Optional[int]:
        __UpperCAmelCase							:	Optional[int]           =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase							:	Union[str, Any]           =  model_class(__lowerCamelCase					)
          __UpperCAmelCase							:	Dict           =  inspect.signature(model.forward					)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          __UpperCAmelCase							:	Union[str, Any]           =  [*signature.parameters.keys()]

          __UpperCAmelCase							:	Union[str, Any]           =  ['''pixel_values''']
          self.assertListEqual(arg_names[:1]		,      __lowerCamelCase					)
      def 	UpperCAmelCase      (      self					:			int					)							->      Tuple:
        __UpperCAmelCase							:	List[str]           =  self.model_tester.prepare_config_and_inputs_for_common()
        __UpperCAmelCase							:	Any           =  True
        __UpperCAmelCase							:	List[str]           =  self.has_attentions

        # no need to test all models as different heads yield the same functionality
        __UpperCAmelCase							:	Tuple           =  self.all_model_classes[0]
        __UpperCAmelCase							:	Optional[Any]           =  model_class(__lowerCamelCase					)
        model.to(__lowerCamelCase					)

        __UpperCAmelCase							:	List[Any]           =  self._prepare_for_class(__lowerCamelCase		,      __lowerCamelCase					)
        __UpperCAmelCase							:	Tuple           =  model(**__lowerCamelCase					)
        __UpperCAmelCase							:	List[str]           =  outputs[0][-1]

        # Encoder-/Decoder-only models
        __UpperCAmelCase							:	Dict           =  outputs.hidden_states[0]
        hidden_states.retain_grad()

        if self.has_attentions:
          __UpperCAmelCase							:	Optional[Any]           =  outputs.attentions[0]
          attentions.retain_grad()

        output.flatten()[0].backward(retain_graph=__lowerCamelCase					)

        self.assertIsNotNone(hidden_states.grad					)

        if self.has_attentions:
          self.assertIsNotNone(attentions.grad					)




      def 	UpperCAmelCase      (      self					:			Tuple					)							->      Any:
        __UpperCAmelCase							:	Tuple           =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          __UpperCAmelCase							:	Dict           =  model_class(__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          __UpperCAmelCase							:	Optional[int]           =  model(**__lowerCamelCase					)

          self.assertEqual(len(result.feature_maps					)		,      len(config.out_indices					)					)
          self.assertEqual(len(model.channels					)		,      len(config.out_indices					)					)

          # Check output of last stage is taken if out_features=None, out_indices=None
          __UpperCAmelCase							:	Tuple           =  copy.deepcopy(__lowerCamelCase					)
          __UpperCAmelCase							:	List[str]           =  None
          __UpperCAmelCase							:	List[str]           =  model_class(__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          __UpperCAmelCase							:	Union[str, Any]           =  model(**__lowerCamelCase					)

          self.assertEqual(len(result.feature_maps					)		,      1					)
          self.assertEqual(len(model.channels					)		,      1					)

          # Check backbone can be initialized with fresh weights
          __UpperCAmelCase							:	int           =  copy.deepcopy(__lowerCamelCase					)
          __UpperCAmelCase							:	Optional[Any]           =  False
          __UpperCAmelCase							:	List[Any]           =  model_class(__lowerCamelCase					)
          model.to(__lowerCamelCase					)
          model.eval()
          __UpperCAmelCase							:	Optional[Any]           =  model(**__lowerCamelCase					)









"""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


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







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Tuple=100				,snake_case_  :  int=" "						)   ->List[str]:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  dict						)   ->dict:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  dict				,snake_case_  :  DPRContextEncoder				,snake_case_  :  DPRContextEncoderTokenizerFast						)   ->dict:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  "RagExampleArguments"				,snake_case_  :  "ProcessingArguments"				,snake_case_  :  "IndexHnswArguments"				,)   ->Tuple:



    '''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    :						str						 =     dataset.map(snake_case_				,batched=snake_case_				,num_proc=processing_args.num_proc						)

    # And compute the embeddings
    __A    :						Union[str, Any]						 =     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=snake_case_						)
    __A    :						str						 =     DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
    __A    :						Optional[int]						 =     Features(
        {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
    __A    :						List[Any]						 =     dataset.map(
        partial(snake_case_				,ctx_encoder=snake_case_				,ctx_tokenizer=snake_case_						)				,batched=snake_case_				,batch_size=processing_args.batch_size				,features=snake_case_				,)

    # And finally save your dataset
    __A    :						int						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset'''						)
    dataset.save_to_disk(snake_case_						)
    # 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    :						Tuple						 =     faiss.IndexHNSWFlat(index_hnsw_args.d				,index_hnsw_args.m				,faiss.METRIC_INNER_PRODUCT						)
    dataset.add_faiss_index('''embeddings'''				,custom_index=snake_case_						)

    # And save the index
    __A    :						List[str]						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset_hnsw_index.faiss'''						)
    dataset.get_index('''embeddings'''						).save(snake_case_						)
    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class 		__snake_case					:





 """simple docstring"""


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






@dataclass
class 		__snake_case					:





 """simple docstring"""


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






@dataclass
class 		__snake_case					:





 """simple docstring"""


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




