




'''simple docstring'''





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

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


if is_flax_available():
   import jax

   from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
   from PIL import Image

   from transformers import BeitImageProcessor

class        _a    (						unittest.TestCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=100 ,_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 ,_SCREAMING_SNAKE_CASE=3 ,)							->      Optional[int]:
          _snake_case							=				parent
          _snake_case							=				vocab_size
          _snake_case							=				batch_size
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				is_training
          _snake_case							=				use_labels
          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				type_sequence_label_size
          _snake_case							=				initializer_range

          # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          _snake_case							=				(image_size // patch_size) ** 2
          _snake_case							=				num_patches + 1






     def     _lowercase     (			self   )							->      Optional[int]:
          _snake_case							=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

          _snake_case							=				None
          if self.use_labels:
               _snake_case							=				ids_tensor([self.batch_size] ,self.type_sequence_label_size   )

          _snake_case							=				BeitConfig(
              vocab_size=self.vocab_size ,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, labels






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          _snake_case							=				FlaxBeitModel(config=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size)   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          _snake_case							=				FlaxBeitForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length - 1, self.vocab_size)   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          _snake_case							=				self.type_sequence_label_size
          _snake_case							=				FlaxBeitForImageClassification(config=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE   )
          self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size)   )

          # test greyscale images
          _snake_case							=				1
          _snake_case							=				FlaxBeitForImageClassification(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
          _snake_case							=				model(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.prepare_config_and_inputs()
          (
              (
              _snake_case
          )							, (
              _snake_case
          )							, (
              _snake_case
          )							, 
          )							=				config_and_inputs
          _snake_case							=				{"pixel_values": pixel_values}
          return config, inputs_dict







@require_flax
class        _a    (						__lowerCAmelCase , unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:   Tuple       =						(
         (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
     )






     def     _lowercase     (			self   )							->      None:
          _snake_case							=				FlaxBeitModelTester(self   )
          _snake_case							=				ConfigTester(self ,config_class=_SCREAMING_SNAKE_CASE ,has_text_modality=_SCREAMING_SNAKE_CASE ,hidden_size=37   )






     def     _lowercase     (			self   )							->      Any:
          self.config_tester.run_common_tests()






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )
               _snake_case							=				inspect.signature(model.__call__   )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               _snake_case							=				[*signature.parameters.keys()]

               _snake_case							=				["pixel_values"]
               self.assertListEqual(arg_names[:1] ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__   ):
                    _snake_case							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				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"   ):
                         _snake_case							=				model_jitted(**_SCREAMING_SNAKE_CASE   ).to_tuple()

                    with self.subTest("JIT Disabled"   ):
                         with jax.disable_jit():
                              _snake_case							=				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   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      int:
          _snake_case							=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      Union[str, Any]:
          for model_class_name in self.all_model_classes:
               _snake_case							=				model_class_name.from_pretrained("microsoft/beit-base-patch16-224"   )
               _snake_case							=				model(np.ones((1, 3, 224, 224)   )   )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def      __a     (       )						->			int:

     """simple docstring"""




     _snake_case							=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
     return image





@require_vision
@require_flax
class        _a    (						unittest.TestCase							):






     @cached_property
     def     _lowercase     (			self   )							->      Optional[Any]:
          return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"   ) if is_vision_available() else None






     @slow
     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"   )

          _snake_case							=				self.default_image_processor
          _snake_case							=				prepare_img()
          _snake_case							=				image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="np"   ).pixel_values

          # prepare bool_masked_pos
          _snake_case							=				np.ones((1, 196) ,dtype=_SCREAMING_SNAKE_CASE   )

          # forward pass
          _snake_case							=				model(pixel_values=_SCREAMING_SNAKE_CASE ,bool_masked_pos=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				outputs.logits

          # verify the logits
          _snake_case							=				(1, 196, 8_192)
          self.assertEqual(logits.shape ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				np.array(
              [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]   )

          self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-2   )   )






     @slow
     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"   )

          _snake_case							=				self.default_image_processor
          _snake_case							=				prepare_img()
          _snake_case							=				image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="np"   )

          # forward pass
          _snake_case							=				model(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				outputs.logits

          # verify the logits
          _snake_case							=				(1, 1_000)
          self.assertEqual(logits.shape ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]   )

          self.assertTrue(np.allclose(logits[0, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-4   )   )

          _snake_case							=				281
          self.assertEqual(logits.argmax(-1   ).item() ,_SCREAMING_SNAKE_CASE   )






     @slow
     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"   )

          _snake_case							=				self.default_image_processor
          _snake_case							=				prepare_img()
          _snake_case							=				image_processor(images=_SCREAMING_SNAKE_CASE ,return_tensors="np"   )

          # forward pass
          _snake_case							=				model(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				outputs.logits

          # verify the logits
          _snake_case							=				(1, 21_841)
          self.assertEqual(logits.shape ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]   )

          self.assertTrue(np.allclose(logits[0, :3] ,_SCREAMING_SNAKE_CASE ,atol=1e-4   )   )

          _snake_case							=				2_396
          self.assertEqual(logits.argmax(-1   ).item() ,_SCREAMING_SNAKE_CASE   )








'''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


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





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Union[str, Any]=100 ,						_UpperCamelCase:   List[str]=" "   )						->			List[str]:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   dict   )						->			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(_UpperCamelCase   ):
                    titles.append(title if title is not None else ""   )
                    texts.append(_UpperCamelCase   )
     return {"title": titles, "text": texts}





def      __a     (       _UpperCamelCase:   dict ,						_UpperCamelCase:   DPRContextEncoder ,						_UpperCamelCase:   DPRContextEncoderTokenizerFast   )						->			dict:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   "RagExampleArguments" ,						_UpperCamelCase:   "ProcessingArguments" ,						_UpperCamelCase:   "IndexHnswArguments" ,						)						->			Dict:

     """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(_UpperCamelCase ,						batched=_UpperCamelCase ,						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=_UpperCamelCase   )
     _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(_UpperCamelCase ,						ctx_encoder=_UpperCamelCase ,						ctx_tokenizer=_UpperCamelCase   ) ,						batched=_UpperCamelCase ,						batch_size=processing_args.batch_size ,						features=_UpperCamelCase ,						)

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







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







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

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


