






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


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







def 			A__							(      _a  :					str		,  _a  :					Dict=100		,  _a  :					int=" "	):





 '''simple docstring'''







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







def 			A__							(      _a  :					dict	):





 '''simple docstring'''







 snake_case__  :    Optional[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 			A__							(      _a  :					dict		,  _a  :					DPRContextEncoder		,  _a  :					DPRContextEncoderTokenizerFast	):





 '''simple docstring'''







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







def 			A__							(      _a  :					"RagExampleArguments"		,  _a  :					"ProcessingArguments"		,  _a  :					"IndexHnswArguments"		,  ):





 '''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__  :    Union[str, 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
 snake_case__  :    List[Any]							       =dataset.map(UpperCamelCase__		,  batched=UpperCamelCase__		,  num_proc=processing_args.num_proc	)

 # And compute the embeddings
 snake_case__  :    Any							       =DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	).to(device=UpperCamelCase__	)
 snake_case__  :    List[str]							       =DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name	)
 snake_case__  :    List[str]							       =Features(
     {"""text""": Value("""string"""	), """title""": Value("""string"""	), """embeddings""": Sequence(Value("""float32"""	)	)}	)  # optional, save as float32 instead of float64 to save space
 snake_case__  :    List[Any]							       =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__  :    Tuple							       =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__  :    Any							       =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__  :    int							       =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  _lowercase   :
      _a							:						str										=							field(
          default=str(Path(__lowercase						).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'						)						,				metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}						,				)
      _a							:						Optional[str]										=							field(
          default=__lowercase						,				metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}						,				)
      _a							:						str										=							field(
          default='facebook/rag-sequence-nq'						,				metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}						,				)
      _a							:						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\''
              )
          }						,				)
      _a							:						Optional[str]										=							field(
          default=str(Path(__lowercase						).parent / 'test_run' / 'dummy-kb'						)						,				metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}						,				)







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







@dataclass
class  _lowercase   :
      _a							:						int										=							field(
          default=768						,				metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,				)
      _a							:						int										=							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)

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













import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
       # Initialising the CNN
       # (Sequential- Building the model layer by layer)
       __lowerCamelCase	:		str										=   models.Sequential()

       # Step 1 - Convolution
       # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
       # (3,3) is the kernel size (filter matrix)
       classifier.add(
           layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="""relu""")
       )

       # Step 2 - Pooling
       classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

       # Adding a second convolutional layer
       classifier.add(layers.ConvaD(32, (3, 3), activation="""relu"""))
       classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

       # Step 3 - Flattening
       classifier.add(layers.Flatten())

       # Step 4 - Full connection
       classifier.add(layers.Dense(units=1_28, activation="""relu"""))
       classifier.add(layers.Dense(units=1, activation="""sigmoid"""))

       # Compiling the CNN
       classifier.compile(
           optimizer="""adam""", loss="""binary_crossentropy""", metrics=["""accuracy"""]
       )

       # Part 2 - Fitting the CNN to the images

       # Load Trained model weights

       # from keras.models import load_model
       # regressor=load_model('cnn.h5')

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

       __lowerCamelCase	:		str										=   train_datagen.flow_from_directory(
           """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
       )

       __lowerCamelCase	:		Optional[Any]										=   test_datagen.flow_from_directory(
           """dataset/test_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
       )

       classifier.fit_generator(
           training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
       )

       classifier.save("""cnn.h5""")

       # Part 3 - Making new predictions

       __lowerCamelCase	:		List[str]										=   tf.keras.preprocessing.image.load_img(
           """dataset/single_prediction/image.png""", target_size=(64, 64)
       )
       __lowerCamelCase	:		List[Any]										=   tf.keras.preprocessing.image.img_to_array(test_image)
       __lowerCamelCase	:		Tuple										=   np.expand_dims(test_image, axis=0)
       __lowerCamelCase	:		Optional[int]										=   classifier.predict(test_image)
       # training_set.class_indices
       if result[0][0] == 0:
              __lowerCamelCase	:		Dict										=   """Normal"""
       if result[0][0] == 1:
              __lowerCamelCase	:		List[Any]										=   """Abnormality detected"""





