





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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_=100       ,		UpperCamelCase_=" "	)  ->		int:
       UpperCamelCase_ 							=	text.split(UpperCamelCase_	)
       return [character.join(text[i : i + n]	).strip() for i in range(0       ,		len(UpperCamelCase_	)       ,		UpperCamelCase_	)]




def 			lowerCAmelCase_			(				UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_							,			UpperCamelCase_ 							=	[], []
       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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[Any]:
       UpperCamelCase_ 							=	load_dataset(
           "csv"       ,		data_files=[csv_path]       ,		split="train"       ,		delimiter="\t"       ,		column_names=["title", "text"]	)
       UpperCamelCase_ 							=	kb_dataset.map(
           UpperCamelCase_       ,		batched=UpperCamelCase_       ,		num_proc=1	)  # if you want you can load already splitted csv.
       UpperCamelCase_ 							=	[kb_dataset.shard(UpperCamelCase_       ,		UpperCamelCase_       ,		contiguous=UpperCamelCase_	) for i in range(UpperCamelCase_	)]
       UpperCamelCase_ 							=	kb_list[process_num]

       UpperCamelCase_ 							=	"data_" + str(UpperCamelCase_	)
       UpperCamelCase_ 							=	os.path.join(UpperCamelCase_       ,		UpperCamelCase_	)

       UpperCamelCase_ 							=	DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"	)
       UpperCamelCase_ 							=	ctx_encoder.to(device=UpperCamelCase_	)

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

       UpperCamelCase_ 							=	Features(
           {"text": Value("string"	), "title": Value("string"	), "embeddings": Sequence(Value("float32"	)	)}	)  # optional, save as float32 instead of float64 to save space

       UpperCamelCase_ 							=	data_shrad.map(
           partial(UpperCamelCase_       ,		ctx_encoder=UpperCamelCase_       ,		ctx_tokenizer=UpperCamelCase_       ,		device=UpperCamelCase_	)       ,		batched=UpperCamelCase_       ,		batch_size=16       ,		features=UpperCamelCase_       ,		)
       dataset.save_to_disk(UpperCamelCase_	)







def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	[]

       for shard_address in glob(str(UpperCamelCase_	) + "/*/"	):
              data_shard_list.append(load_from_disk(UpperCamelCase_	)	)

       UpperCamelCase_ 							=	concatenate_datasets(UpperCamelCase_	)
       faiss.omp_set_num_threads(96	)

       UpperCamelCase_ 							=	faiss.IndexHNSWFlat(768       ,		128       ,		faiss.METRIC_INNER_PRODUCT	)
       concat.add_faiss_index("embeddings"       ,		custom_index=UpperCamelCase_	)
       concat.get_index("embeddings"	).save(
           UpperCamelCase_	)  # since we load the index in to memory,we can directly update the index in the disk






import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						UpperCAmelCase__     (     __UpperCamelCase		,unittest.TestCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			HerbertTokenizer
			UpperCamelCase     		=			HerbertTokenizerFast
			UpperCamelCase     		=			True




			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					super().setUp()

					# Use a simpler test file without japanese/chinese characters
					with open(F'{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'			,			encoding='''utf-8'''					) as f_data:
							__UpperCAmelCase       :	List[str]						=      f_data.read().replace('''\n\n'''			,			'''\n'''					).strip()

					__UpperCAmelCase       :	Dict						=      [
					    '''<s>''',
					    '''</s>''',
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''w</w>''',
					    '''r</w>''',
					    '''t</w>''',
					    '''lo''',
					    '''low''',
					    '''er</w>''',
					    '''low</w>''',
					    '''lowest</w>''',
					    '''newer</w>''',
					    '''wider</w>''',
					    ''',</w>''',
					    '''<unk>''',
					]
					__UpperCAmelCase       :	Tuple						=      dict(zip(a_			,			range(len(a_					)					)					)					)
					__UpperCAmelCase       :	Optional[int]						=      ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

					__UpperCAmelCase       :	str						=      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file''']					)
					__UpperCAmelCase       :	List[str]						=      os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file''']					)
					with open(self.vocab_file			,			'''w'''					) as fp:
							fp.write(json.dumps(a_					)					)
					with open(self.merges_file			,			'''w'''					) as fp:
							fp.write('''\n'''.join(a_					)					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[str]						=      '''lower newer'''
					__UpperCAmelCase       :	List[Any]						=      '''lower newer'''
					return input_text, output_text




			def 					snake_case__	(  self				:					int					):
					'''simple docstring'''





					__UpperCAmelCase       :	Any						=      self.tokenizer_class(vocab_file=self.vocab_file			,			merges_file=self.merges_file					)

					__UpperCAmelCase       :	Optional[int]						=      '''lower'''
					__UpperCAmelCase       :	Any						=      ['''low''', '''er</w>''']
					__UpperCAmelCase       :	Any						=      tokenizer.tokenize(a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	int						=      tokens + ['''<unk>''']
					__UpperCAmelCase       :	Optional[Any]						=      [16, 17, 23]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(a_					)			,			a_					)




			def 					snake_case__	(  self				:					List[Any]					):
					'''simple docstring'''





					if not self.test_rust_tokenizer:
							return

					__UpperCAmelCase       :	str						=      self.get_tokenizer()
					__UpperCAmelCase       :	Optional[int]						=      self.get_rust_tokenizer()

					__UpperCAmelCase       :	Tuple						=      '''lower,newer'''

					__UpperCAmelCase       :	Optional[int]						=      tokenizer.tokenize(a_					)
					__UpperCAmelCase       :	Tuple						=      rust_tokenizer.tokenize(a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	Optional[Any]						=      tokenizer.encode(a_			,			add_special_tokens=a_					)
					__UpperCAmelCase       :	Union[str, Any]						=      rust_tokenizer.encode(a_			,			add_special_tokens=a_					)
					self.assertListEqual(a_			,			a_					)

					__UpperCAmelCase       :	Tuple						=      self.get_rust_tokenizer()
					__UpperCAmelCase       :	Union[str, Any]						=      tokenizer.encode(a_					)
					__UpperCAmelCase       :	str						=      rust_tokenizer.encode(a_					)
					self.assertListEqual(a_			,			a_					)




			@slow
			def 					snake_case__	(  self				:					Tuple					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''					)

					__UpperCAmelCase       :	Tuple						=      tokenizer.encode('''konstruowanie sekwencji'''			,			add_special_tokens=a_					)
					__UpperCAmelCase       :	int						=      tokenizer.encode('''konstruowanie wielu sekwencji'''			,			add_special_tokens=a_					)

					__UpperCAmelCase       :	Union[str, Any]						=      tokenizer.build_inputs_with_special_tokens(a_					)
					__UpperCAmelCase       :	Any						=      tokenizer.build_inputs_with_special_tokens(a_			,			a_					)

					assert encoded_sentence == [0] + text + [2]
					assert encoded_pair == [0] + text + [2] + text_a + [2]





