





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








_SCREAMING_SNAKE_CASE   						=		{
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

_SCREAMING_SNAKE_CASE   						=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_SCREAMING_SNAKE_CASE   						=		[0 for x in range(len(graph))]
_SCREAMING_SNAKE_CASE   						=		[]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      Any           =						1
	while stack:
		snake_case_		:      List[Any]           =						stack.pop()
		print(__a ,			clothes[current_clothing] )
		order += 1


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	snake_case_		:      int           =						1
	for v in graph[u]:
		if not visited[v]:
			depth_first_search(__a ,			__a ,			__a )

	stack.append(__a )


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	for v in range(len(__a ) ):
		if not visited[v]:
			depth_first_search(__a ,			__a ,			__a )


if __name__ == "__main__":
						topological_sort(graph, visited)
						print(stack)
						print_stack(stack, clothes)

