



"""simple docstring"""







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class   UpperCamelCase	(     snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       str						       =  BartTokenizer
	SCREAMING_SNAKE_CASE_							:       Optional[int]						       =  BartTokenizerFast
	SCREAMING_SNAKE_CASE_							:       Dict						       =  True
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  filter_roberta_detectors






	def    lowerCamelCase__					(     self   ):
			super().setUp()
			_lowercase				: Tuple     =   [
			    """l""",
			    """o""",
			    """w""",
			    """e""",
			    """r""",
			    """s""",
			    """t""",
			    """i""",
			    """d""",
			    """n""",
			    """\u0120""",
			    """\u0120l""",
			    """\u0120n""",
			    """\u0120lo""",
			    """\u0120low""",
			    """er""",
			    """\u0120lowest""",
			    """\u0120newer""",
			    """\u0120wider""",
			    """<unk>""",
			]
			_lowercase				: Tuple     =   dict(zip(UpperCAmelCase_				,range(len(UpperCAmelCase_   )   )   )   )
			_lowercase				: Tuple     =   ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
			_lowercase				: Any     =   {"""unk_token""": """<unk>"""}

			_lowercase				: int     =   os.path.join(self.tmpdirname				,VOCAB_FILES_NAMES["""vocab_file"""]   )
			_lowercase				: Tuple     =   os.path.join(self.tmpdirname				,VOCAB_FILES_NAMES["""merges_file"""]   )
			with open(self.vocab_file				,"""w"""				,encoding="""utf-8"""   ) as fp:
					fp.write(json.dumps(UpperCAmelCase_   ) + """\n"""   )
			with open(self.merges_file				,"""w"""				,encoding="""utf-8"""   ) as fp:
					fp.write("""\n""".join(UpperCAmelCase_   )   )






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			kwargs.update(self.special_tokens_map   )
			return self.tokenizer_class.from_pretrained(self.tmpdirname				,**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,**UpperCAmelCase_   ):
			kwargs.update(self.special_tokens_map   )
			return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,**UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			return "lower newer", "lower newer"






	@cached_property
	def    lowerCamelCase__					(     self   ):
			return BartTokenizer.from_pretrained("""facebook/bart-large"""   )






	@cached_property
	def    lowerCamelCase__					(     self   ):
			return BartTokenizerFast.from_pretrained("""facebook/bart-large"""   )






	@require_torch
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
			_lowercase				: Any     =   [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					_lowercase				: Optional[Any]     =   tokenizer(UpperCAmelCase_				,max_length=len(UpperCAmelCase_   )				,padding=UpperCAmelCase_				,return_tensors="""pt"""   )
					self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )

					self.assertEqual((2, 9)				,batch.input_ids.shape   )
					self.assertEqual((2, 9)				,batch.attention_mask.shape   )
					_lowercase				: str     =   batch.input_ids.tolist()[0]
					self.assertListEqual(UpperCAmelCase_				,UpperCAmelCase_   )
					# Test that special tokens are reset






	@require_torch
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					_lowercase				: Union[str, Any]     =   tokenizer(UpperCAmelCase_				,padding=UpperCAmelCase_				,return_tensors="""pt"""   )
					# check if input_ids are returned and no labels
					self.assertIn("""input_ids"""				,UpperCAmelCase_   )
					self.assertIn("""attention_mask"""				,UpperCAmelCase_   )
					self.assertNotIn("""labels"""				,UpperCAmelCase_   )
					self.assertNotIn("""decoder_attention_mask"""				,UpperCAmelCase_   )






	@require_torch
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   [
			    """Summary of the text.""",
			    """Another summary.""",
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					_lowercase				: Optional[int]     =   tokenizer(text_target=UpperCAmelCase_				,max_length=32				,padding="""max_length"""				,return_tensors="""pt"""   )
					self.assertEqual(32				,targets["""input_ids"""].shape[1]   )






	@require_torch
	def    lowerCamelCase__					(     self   ):
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					_lowercase				: Tuple     =   tokenizer(
					    ["""I am a small frog""" * 10_24, """I am a small frog"""]				,padding=UpperCAmelCase_				,truncation=UpperCAmelCase_				,return_tensors="""pt"""   )
					self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
					self.assertEqual(batch.input_ids.shape				,(2, 10_24)   )






	@require_torch
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   ["""A long paragraph for summarization."""]
			_lowercase				: Any     =   [
			    """Summary of the text.""",
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					_lowercase				: Dict     =   tokenizer(UpperCAmelCase_				,return_tensors="""pt"""   )
					_lowercase				: List[str]     =   tokenizer(text_target=UpperCAmelCase_				,return_tensors="""pt"""   )
					_lowercase				: Optional[int]     =   inputs["""input_ids"""]
					_lowercase				: Optional[int]     =   targets["""input_ids"""]
					self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()   )
					self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()   )
					self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()   )
					self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()   )






	def    lowerCamelCase__					(     self   ):
			pass






	def    lowerCamelCase__					(     self   ):
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
							_lowercase				: Optional[Any]     =   self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_				,**UpperCAmelCase_   )
							_lowercase				: List[str]     =   self.tokenizer_class.from_pretrained(UpperCAmelCase_				,**UpperCAmelCase_   )
							_lowercase				: List[Any]     =   """A, <mask> AllenNLP sentence."""
							_lowercase				: Union[str, Any]     =   tokenizer_r.encode_plus(UpperCAmelCase_				,add_special_tokens=UpperCAmelCase_				,return_token_type_ids=UpperCAmelCase_   )
							_lowercase				: List[Any]     =   tokenizer_p.encode_plus(UpperCAmelCase_				,add_special_tokens=UpperCAmelCase_				,return_token_type_ids=UpperCAmelCase_   )

							# token_type_ids should put 0 everywhere
							self.assertEqual(sum(tokens_r["""token_type_ids"""]   )				,sum(tokens_p["""token_type_ids"""]   )   )

							# attention_mask should put 1 everywhere, so sum over length should be 1
							self.assertEqual(
							    sum(tokens_r["""attention_mask"""]   ) / len(tokens_r["""attention_mask"""]   )				,sum(tokens_p["""attention_mask"""]   ) / len(tokens_p["""attention_mask"""]   )				,)

							_lowercase				: Tuple     =   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]   )
							_lowercase				: List[str]     =   tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]   )

							# Rust correctly handles the space before the mask while python doesnt
							self.assertSequenceEqual(tokens_p["""input_ids"""]				,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )
							self.assertSequenceEqual(tokens_r["""input_ids"""]				,[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]   )

							self.assertSequenceEqual(
							    UpperCAmelCase_				,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )
							self.assertSequenceEqual(
							    UpperCAmelCase_				,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]   )




'''simple docstring'''





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__  (				snake_case       :				List[Any]				,	snake_case       :				List[str]=100				,	snake_case       :				Any=" "  )   ->				Any:







     """simple docstring"""
     a					:							Union[str, Any]							= text.split(snake_case  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0				,	len(snake_case  )				,	snake_case  )]






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[int]  )   ->				Union[str, Any]:







     """simple docstring"""
     a							,			a					:							int							= [], []
     for title, text in zip(documents['title']				,	documents['text']  ):
          if text is not None:
               for passage in split_text(snake_case  ):
                    titles.append(title if title is not None else ''  )
                    texts.append(snake_case  )
     return {"title": titles, "text": texts}






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				str				,	snake_case       :				List[Any]				,	snake_case       :				Dict				,	snake_case       :				Union[str, Any]				,	snake_case       :				int  )   ->				Dict:







     """simple docstring"""
     a					:							Tuple							= load_dataset(
         'csv'				,	data_files=[csv_path]				,	split='train'				,	delimiter='\t'				,	column_names=['title', 'text']  )
     a					:							Union[str, Any]							= kb_dataset.map(
         snake_case				,	batched=snake_case				,	num_proc=1  )  # if you want you can load already splitted csv.
     a					:							Optional[Any]							= [kb_dataset.shard(snake_case				,	snake_case				,	contiguous=snake_case  ) for i in range(snake_case  )]
     a					:							List[Any]							= kb_list[process_num]

     a					:							Dict							= 'data_' + str(snake_case  )
     a					:							int							= os.path.join(snake_case				,	snake_case  )

     a					:							Any							= DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
     a					:							List[str]							= ctx_encoder.to(device=snake_case  )

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

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

     a					:							List[str]							= data_shrad.map(
         partial(snake_case				,	ctx_encoder=snake_case				,	ctx_tokenizer=snake_case				,	device=snake_case  )				,	batched=snake_case				,	batch_size=16				,	features=snake_case				,	)
     dataset.save_to_disk(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Any  )   ->				List[Any]:







     """simple docstring"""
     a					:							int							= []

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

     a					:							Tuple							= concatenate_datasets(snake_case  )
     faiss.omp_set_num_threads(96  )

     a					:							List[str]							= faiss.IndexHNSWFlat(768				,	128				,	faiss.METRIC_INNER_PRODUCT  )
     concat.add_faiss_index('embeddings'				,	custom_index=snake_case  )
     concat.get_index('embeddings'  ).save(
         snake_case  )  # since we load the index in to memory,we can directly update the index in the disk

