


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



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Optional[int]=1_00 ,       UpperCAmelCase_       :							Any=" "				)	->						List[str]:
     __lowerCamelCase  :      Any														=						text.split(UpperCAmelCase_				)
     return [character.join(text[i : i + n]				).strip() for i in range(0 ,       len(UpperCAmelCase_				) ,       UpperCAmelCase_				)]



def      UpperCAmelCase__   (      UpperCAmelCase_       :							dict				)	->						dict:
     __lowerCamelCase							,			__lowerCamelCase  :      List[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      UpperCAmelCase__   (      UpperCAmelCase_       :							dict ,       UpperCAmelCase_       :							DPRContextEncoder ,       UpperCAmelCase_       :							DPRContextEncoderTokenizerFast				)	->						dict:
     __lowerCamelCase  :      Union[str, Any]														=						ctx_tokenizer(
         documents['title'] ,       documents['text'] ,       truncation=UpperCAmelCase_ ,       padding='longest' ,       return_tensors='pt'				)['input_ids']
     __lowerCamelCase  :      Tuple														=						ctx_encoder(input_ids.to(device=UpperCAmelCase_				) ,       return_dict=UpperCAmelCase_				).pooler_output
     return {"embeddings": embeddings.detach().cpu().numpy()}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							"RagExampleArguments" ,       UpperCAmelCase_       :							"ProcessingArguments" ,       UpperCAmelCase_       :							"IndexHnswArguments" ,       )	->						Dict:
     ######################################
     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  :      int														=						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  :      str														=						dataset.map(UpperCAmelCase_ ,       batched=UpperCAmelCase_ ,       num_proc=processing_args.num_proc				)

     # And compute the embeddings
     __lowerCamelCase  :      Union[str, Any]														=						DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				).to(device=UpperCAmelCase_				)
     __lowerCamelCase  :      Dict														=						DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				)
     __lowerCamelCase  :      Any														=						Features(
         {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space
     __lowerCamelCase  :      List[str]														=						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  :      Dict														=						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  :      int														=						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  :      Union[str, Any]														=						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   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str					=     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\''}   ,						)
    lowerCamelCase    :   Optional[str]					=     field(
        default=_UpperCAmelCase   ,						metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}   ,						)
    lowerCamelCase    :   str					=     field(
        default='facebook/rag-sequence-nq'   ,						metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}   ,						)
    lowerCamelCase    :   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\''
            )
        }   ,						)
    lowerCamelCase    :   Optional[str]					=     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   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   Optional[int]					=     field(
        default=_UpperCAmelCase   ,						metadata={
            'help': 'The number of processes to use to split the documents into passages. Default is single process.'
        }   ,						)
    lowerCamelCase    :   int					=     field(
        default=1_6   ,						metadata={
            'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
        }   ,						)







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     field(
        default=7_6_8   ,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}   ,						)
    lowerCamelCase    :   int					=     field(
        default=1_2_8   ,						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__		,   A__		,   A__						:   Tuple				=    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)










'''simple docstring'''



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel





class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Optional[int]:
         __lowerCamelCase  :      Optional[int]														=						parent
         __lowerCamelCase  :      Dict														=						batch_size
         __lowerCamelCase  :      int														=						image_size
         __lowerCamelCase  :      List[str]														=						patch_size
         __lowerCamelCase  :      Optional[int]														=						num_channels
         __lowerCamelCase  :      Any														=						is_training
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      Optional[Any]														=						num_attention_heads
         __lowerCamelCase  :      Dict														=						intermediate_size
         __lowerCamelCase  :      Union[str, Any]														=						hidden_act
         __lowerCamelCase  :      Optional[int]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      str														=						type_sequence_label_size
         __lowerCamelCase  :      List[str]														=						initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __lowerCamelCase  :      str														=						(image_size // patch_size) ** 2
         __lowerCamelCase  :      Optional[int]														=						num_patches + 1







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      int														=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __lowerCamelCase  :      Optional[int]														=						ViTConfig(
             image_size=self.image_size     ,	patch_size=self.patch_size     ,	num_channels=self.num_channels     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	num_attention_heads=self.num_attention_heads     ,	intermediate_size=self.intermediate_size     ,	hidden_act=self.hidden_act     ,	hidden_dropout_prob=self.hidden_dropout_prob     ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,	is_decoder=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	)

         return config, pixel_values







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         __lowerCamelCase  :      Union[str, Any]														=						FlaxViTModel(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      str														=						model(SCREAMING_SNAKE_CASE_      )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __lowerCamelCase  :      str														=						(self.image_size, self.image_size)
         __lowerCamelCase  :      str														=						(self.patch_size, self.patch_size)
         __lowerCamelCase  :      Any														=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, num_patches + 1, self.hidden_size)      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      Tuple														=						self.type_sequence_label_size
         __lowerCamelCase  :      Any														=						FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         __lowerCamelCase  :      List[str]														=						1
         __lowerCamelCase  :      List[Any]														=						FlaxViTForImageClassification(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      List[Any]														=						model(SCREAMING_SNAKE_CASE_      )







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







@require_flax
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   str					=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







    def  lowercase_							(				self      )							->	None:
         __lowerCamelCase  :      str														=						FlaxViTModelTester(self      )
         __lowerCamelCase  :      Union[str, Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37      )







    def  lowercase_							(				self      )							->	List[Any]:
         self.config_tester.run_common_tests()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[Any]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __lowerCamelCase  :      Optional[Any]														=						model_class(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Dict														=						inspect.signature(model.__call__      )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowerCamelCase  :      List[str]														=						[*signature.parameters.keys()]

              __lowerCamelCase  :      Optional[int]														=						['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__      ):
                   __lowerCamelCase  :      List[Any]														=						self._prepare_for_class(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

                   @jax.jit
                   def model_jitted(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      ):
                        return model(pixel_values=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

                   with self.subTest('JIT Enabled'      ):
                        __lowerCamelCase  :      Optional[int]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

                   with self.subTest('JIT Disabled'      ):
                        with jax.disable_jit():
                             __lowerCamelCase  :      Union[str, Any]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

                   self.assertEqual(len(SCREAMING_SNAKE_CASE_      )     ,	len(SCREAMING_SNAKE_CASE_      )      )
                   for jitted_output, output in zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                        self.assertEqual(jitted_output.shape     ,	output.shape      )







    @slow
    def  lowercase_							(				self      )							->	List[Any]:
         for model_class_name in self.all_model_classes:
              __lowerCamelCase  :      Union[str, Any]														=						model_class_name.from_pretrained('google/vit-base-patch16-224'      )
              __lowerCamelCase  :      Union[str, Any]														=						model(np.ones((1, 3, 2_24, 2_24)      )      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )






