


import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Dict      ,						lowercase__  :str=1_0_0      ,						lowercase__  :Tuple=" "				)       ->  List[Any]:
						__magic_name__				:							Union[str, Any]				=      text.split(lowercase__				)
						return [character.join(text[i : i + n]				).strip() for i in range(0      ,						len(lowercase__				)      ,						lowercase__				)]
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :List[Any]				)       ->  Dict:
						__magic_name__		, __magic_name__				:							Tuple				=      [], []
						for title, text in zip(documents['title']      ,						documents['text']				):
												if text is not None:
																		for passage in split_text(lowercase__				):
																								titles.append(title if title is not None else ''				)
																								texts.append(lowercase__				)
						return {"title": titles, "text": texts}
def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Tuple      ,						lowercase__  :Union[str, Any]      ,						lowercase__  :List[str]      ,						lowercase__  :int      ,						lowercase__  :Tuple      ,						lowercase__  :Optional[int]				)       ->  Dict:
						__magic_name__				:							Any				=      load_dataset(
						    'csv'      ,						data_files=[csv_path]      ,						split='train'      ,						delimiter='\t'      ,						column_names=['title', 'text']				)
						__magic_name__				:							int				=      kb_dataset.map(
						    lowercase__      ,						batched=lowercase__      ,						num_proc=1				)  # if you want you can load already splitted csv.
						__magic_name__				:							int				=      [kb_dataset.shard(lowercase__      ,						lowercase__      ,						contiguous=lowercase__				) for i in range(lowercase__				)]
						__magic_name__				:							List[Any]				=      kb_list[process_num]

						__magic_name__				:							Dict				=      'data_' + str(lowercase__				)
						__magic_name__				:							Union[str, Any]				=      os.path.join(lowercase__      ,						lowercase__				)

						__magic_name__				:							Any				=      DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'				)
						__magic_name__				:							Tuple				=      ctx_encoder.to(device=lowercase__				)

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

						__magic_name__				:							Optional[Any]				=      Features(
						    {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space

						__magic_name__				:							List[str]				=      data_shrad.map(
						    partial(lowercase__      ,						ctx_encoder=lowercase__      ,						ctx_tokenizer=lowercase__      ,						device=lowercase__				)      ,						batched=lowercase__      ,						batch_size=1_6      ,						features=lowercase__      ,						)
						dataset.save_to_disk(lowercase__				)

def  __SCREAMING_SNAKE_CASE       (				lowercase__  :Optional[int]      ,						lowercase__  :str				)       ->  Dict:
						__magic_name__				:							int				=      []

						for shard_address in glob(str(lowercase__				) + '/*/'				):
												data_shard_list.append(load_from_disk(lowercase__				)				)

						__magic_name__				:							int				=      concatenate_datasets(lowercase__				)
						faiss.omp_set_num_threads(9_6				)

						__magic_name__				:							Optional[Any]				=      faiss.IndexHNSWFlat(7_6_8      ,						1_2_8      ,						faiss.METRIC_INNER_PRODUCT				)
						concat.add_faiss_index('embeddings'      ,						custom_index=lowercase__				)
						concat.get_index('embeddings'				).save(
						    lowercase__				)  # since we load the index in to memory,we can directly update the index in the disk






from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
snake_case__     							= load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
snake_case__							,     snake_case__							,     snake_case__							,     snake_case__     							= train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

snake_case__     							= KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
snake_case__     							= [[1, 2, 1, 4], [2, 3, 4, 5]]

snake_case__     							= knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris['target_names'][prediction]}"""
)


