


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	(       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	Dict=100     ,       lowerCamelCase__		:	List[str]=" "			):





			'''simple docstring'''




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






def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]			):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase								=     [], []
			for title, text in zip(documents["""title"""]     ,       documents["""text"""]			):
						if text is not None:
									for passage in split_text(lowerCamelCase__			):
												titles.append(title if title is not None else """"""			)
												texts.append(lowerCamelCase__			)
			return {"title": titles, "text": texts}






def   __lowerCamelCase	(       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	Any     ,       lowerCamelCase__		:	int     ,       lowerCamelCase__		:	List[Any]     ,       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	Union[str, Any]			):





			'''simple docstring'''




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

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

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

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

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

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






def   __lowerCamelCase	(       lowerCamelCase__		:	Dict     ,       lowerCamelCase__		:	int			):





			'''simple docstring'''




			lowerCamelCase								=     []

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

			lowerCamelCase								=     concatenate_datasets(lowerCamelCase__			)
			faiss.omp_set_num_threads(96			)

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





import unittest

from cer import CER


UpperCAmelCase      :						Union[str, Any]    =		CER()







class 					__lowercase					(       unittest.TestCase     ):



		"""simple docstring"""

		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""White House"""]
					lowerCamelCase								=     ["""white house"""]
					# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1818181818  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""werewolf"""]
					# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.1111111  ) < 1e-6  )

					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weae     wolf"""]
					# S = 1, D = 1, I = 0, N = 8, CER = 0.25
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.25  ) < 1e-6  )

					# consecutive whitespaces case 1
					lowerCamelCase								=     ["""were wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )

					# consecutive whitespaces case 2
					lowerCamelCase								=     ["""were   wolf"""]
					lowerCamelCase								=     ["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.0  ) < 1e-6  )







		def 				__A      (   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""weaewolf"""]
					# S = 1, D = 0, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereawolf"""]
					# S = 0, D = 1, I = 0, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     ["""wereolf"""]
					# S = 0, D = 0, I = 1, N = 8, CER = 0.125
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.125  ) < 1e-6  )







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )







		def 				__A      (   self  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     ["""werewolf""", """I am your father"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertEqual(A		,			0.0  )

					lowerCamelCase								=     ["""werewolf""", """I am your father""", """doge"""]
					lowerCamelCase								=     ["""werxwolf""", """I       am your father""", """doge"""]
					# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.03571428  ) < 1e-6  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     ["""My hovercraft""", """is full of eels"""]
					lowerCamelCase								=     ["""My hovercraft is full""", """ of eels"""]
					# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
					# whitespace at the front of " of eels" will be strip during preporcessing
					# so need to insert 2 whitespaces
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A		,			concatenate_texts=A  )
					self.assertTrue(abs(char_error_rate - 0.071428  ) < 1e-6  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     [""" 能吞虾玻璃而 不霜身体啦"""]
					# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.4545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃""", """而不伤身体"""]
					lowerCamelCase								=     ["""我    能 吞 下 玻 璃""", """而不伤身体"""]
					# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertTrue(abs(char_error_rate - 0.454545454545  ) < 1e-6  )

					lowerCamelCase								=     ["""我能吞下玻璃而不伤身体"""]
					lowerCamelCase								=     cer.compute(predictions=A		,			references=A  )
					self.assertFalse(A		,			0.0  )







		def 				__A      (   self  )     ->   Optional[int]:







					'''simple docstring'''







					lowerCamelCase								=     [""""""]
					lowerCamelCase								=     ["""Hypothesis"""]
					with self.assertRaises(A  ):
								cer.compute(predictions=A		,			references=A  )


if __name__ == "__main__":
		unittest.main()

