





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  (    UpperCamelCase__      ,      UpperCamelCase__=100      ,      UpperCamelCase__=" "			)  ->    Any:







					'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







					'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Tuple:







					'''simple docstring'''

					UpperCAmelCase       		=    load_dataset(
					    '''csv'''      ,      data_files=[csv_path]      ,      split='''train'''      ,      delimiter='''\t'''      ,      column_names=['''title''', '''text''']			)
					UpperCAmelCase       		=    kb_dataset.map(
					    A__      ,      batched=A__      ,      num_proc=1			)  # if you want you can load already splitted csv.
					UpperCAmelCase       		=    [kb_dataset.shard(A__      ,      A__      ,      contiguous=A__			) for i in range(A__			)]
					UpperCAmelCase       		=    kb_list[process_num]

					UpperCAmelCase       		=    'data_' + str(A__			)
					UpperCAmelCase       		=    os.path.join(A__      ,      A__			)

					UpperCAmelCase       		=    DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''			)
					UpperCAmelCase       		=    ctx_encoder.to(device=A__			)

					def embed(
					    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			) -> dict:
										UpperCAmelCase       		=    ctx_tokenizer(
										    documents['''title''']      ,      documents['''text''']      ,      truncation=A__      ,      padding='''longest'''      ,      return_tensors='''pt'''			)['input_ids']
										UpperCAmelCase       		=    ctx_encoder(input_ids.to(device=A__			)      ,      return_dict=A__			).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(A__      ,      ctx_encoder=A__      ,      ctx_tokenizer=A__      ,      device=A__			)      ,      batched=A__      ,      batch_size=16      ,      features=A__      ,      )
					dataset.save_to_disk(A__			)






def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







					'''simple docstring'''

					UpperCAmelCase       		=    []

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

					UpperCAmelCase       		=    concatenate_datasets(A__			)
					faiss.omp_set_num_threads(96			)

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










import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard

class      A_	(unittest.TestCase      ):
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    {
				    '''model_details''': {
				        '''Organization''': '''testing''',
				        '''Model date''': '''today''',
				        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
				        '''Architecture''': '''Convolutional Neural Network.''',
				    },
				    '''metrics''': '''BLEU and ROUGE-1''',
				    '''evaluation_data''': {
				        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
				        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
				    },
				    '''training_data''': {
				        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
				        '''Preprocessing''': (
				            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
				            ''' https://arxiv.org/pdf/1810.03993.pdf'''
				        ),
				    },
				    '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 7_6},
				}
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				self.assertTrue(hasattr(_A							, '''model_details'''		)		)
				self.assertTrue(hasattr(_A							, '''intended_use'''		)		)
				self.assertTrue(hasattr(_A							, '''factors'''		)		)
				self.assertTrue(hasattr(_A							, '''metrics'''		)		)
				self.assertTrue(hasattr(_A							, '''evaluation_data'''		)		)
				self.assertTrue(hasattr(_A							, '''training_data'''		)		)
				self.assertTrue(hasattr(_A							, '''quantitative_analyses'''		)		)
				self.assertTrue(hasattr(_A							, '''ethical_considerations'''		)		)
				self.assertTrue(hasattr(_A							, '''caveats_and_recommendations'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)
				UpperCAmelCase       		=    json.loads(modelcard.to_json_string()		)
				for key, value in self.inputs_dict.items():
							self.assertEqual(obj[key]							, _A		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							UpperCAmelCase       		=    os.path.join(_A							, '''modelcard.json'''		)
							model_card_first.to_json_file(_A		)
							UpperCAmelCase       		=    ModelCard.from_json_file(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    ModelCard.from_dict(self.inputs_dict		)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model_card_first.save_pretrained(_A		)
							UpperCAmelCase       		=    ModelCard.from_pretrained(_A		)

				self.assertEqual(model_card_second.to_dict()							, model_card_first.to_dict()		)



