



"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
			from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
			__SCREAMING_SNAKE_CASE     :  Optional[int]										=					None

if is_ray_available():
			import ray  # noqa: E402 # isort:skip
			from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
			__SCREAMING_SNAKE_CASE     :  Union[str, Any]										=					None
			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					None
			__SCREAMING_SNAKE_CASE     :  Any										=					None



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				str:
				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								_lowerCamelCase					     = unittest.skip('''test requires Datasets, Faiss, psutil'''							)(lowercase_							)
				return test_case




@require_distributed_retrieval
class 							lowerCamelCase_(      A__			):





					'''simple docstring'''



					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = tempfile.mkdtemp()
									_lowerCamelCase					     = 8

									# DPR tok
									_lowerCamelCase					     = [
									    '''[UNK]''',
									    '''[CLS]''',
									    '''[SEP]''',
									    '''[PAD]''',
									    '''[MASK]''',
									    '''want''',
									    '''##want''',
									    '''##ed''',
									    '''wa''',
									    '''un''',
									    '''runn''',
									    '''##ing''',
									    ''',''',
									    '''low''',
									    '''lowest''',
									]
									_lowerCamelCase					     = os.path.join(self.tmpdirname ,				'''dpr_tokenizer'''      )
									os.makedirs(lowerCamelCase__ ,				exist_ok=lowerCamelCase__      )
									_lowerCamelCase					     = os.path.join(lowerCamelCase__ ,				DPR_VOCAB_FILES_NAMES['''vocab_file''']      )
									with open(self.vocab_file ,				'''w''' ,				encoding='''utf-8'''      ) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )

									# BART tok
									_lowerCamelCase					     = [
									    '''l''',
									    '''o''',
									    '''w''',
									    '''e''',
									    '''r''',
									    '''s''',
									    '''t''',
									    '''i''',
									    '''d''',
									    '''n''',
									    '''\u0120''',
									    '''\u0120l''',
									    '''\u0120n''',
									    '''\u0120lo''',
									    '''\u0120low''',
									    '''er''',
									    '''\u0120lowest''',
									    '''\u0120newer''',
									    '''\u0120wider''',
									    '''<unk>''',
									]
									_lowerCamelCase					     = dict(zip(lowerCamelCase__ ,				range(len(lowerCamelCase__      )      )      )      )
									_lowerCamelCase					     = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
									_lowerCamelCase					     = {'''unk_token''': '''<unk>'''}

									_lowerCamelCase					     = os.path.join(self.tmpdirname ,				'''bart_tokenizer'''      )
									os.makedirs(lowerCamelCase__ ,				exist_ok=lowerCamelCase__      )
									_lowerCamelCase					     = os.path.join(lowerCamelCase__ ,				BART_VOCAB_FILES_NAMES['''vocab_file''']      )
									_lowerCamelCase					     = os.path.join(lowerCamelCase__ ,				BART_VOCAB_FILES_NAMES['''merges_file''']      )
									with open(self.vocab_file ,				'''w''' ,				encoding='''utf-8'''      ) as fp:
													fp.write(json.dumps(lowerCamelCase__      ) + '''\n'''      )
									with open(self.merges_file ,				'''w''' ,				encoding='''utf-8'''      ) as fp:
													fp.write('''\n'''.join(lowerCamelCase__      )      )

					def 	snake_case__					(			self      ):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,				'''dpr_tokenizer'''      )      )

					def 	snake_case__					(			self      ):
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,				'''bart_tokenizer'''      )      )

					def 	snake_case__					(			self      ):
									shutil.rmtree(self.tmpdirname      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = Dataset.from_dict(
									    {
									        '''id''': ['''0''', '''1'''],
									        '''text''': ['''foo''', '''bar'''],
									        '''title''': ['''Foo''', '''Bar'''],
									        '''embeddings''': [np.ones(self.retrieval_vector_size      ), 2 * np.ones(self.retrieval_vector_size      )],
									    }      )
									dataset.add_faiss_index('''embeddings''' ,				string_factory='''Flat''' ,				metric_type=faiss.METRIC_INNER_PRODUCT      )
									return dataset

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_2_3_4_5      ):
									_lowerCamelCase					     = self.get_dummy_dataset()
									_lowerCamelCase					     = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size ,				question_encoder=DPRConfig().to_dict() ,				generator=BartConfig().to_dict() ,				)
									with patch('''transformers.models.rag.retrieval_rag.load_dataset'''      ) as mock_load_dataset:
													_lowerCamelCase					     = dataset
													_lowerCamelCase					     = RagPyTorchDistributedRetriever(
													    lowerCamelCase__ ,				question_encoder_tokenizer=self.get_dpr_tokenizer() ,				generator_tokenizer=self.get_bart_tokenizer() ,				)
													if init_retrieval:
																	retriever.init_retrieval(lowerCamelCase__      )
									return retriever

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									# Have to run in local mode because sys.path modifications at top of
									# file are not propogated to remote workers.
									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
									ray.init(local_mode=lowerCamelCase__      )
									_lowerCamelCase					     = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size ,				question_encoder=DPRConfig().to_dict() ,				generator=BartConfig().to_dict() ,				)
									_lowerCamelCase					     = ray.remote(lowerCamelCase__      )
									_lowerCamelCase					     = [remote_cls.remote() for _ in range(1      )]
									with patch('''transformers.models.rag.retrieval_rag.load_dataset'''      ) as mock_load_dataset:
													_lowerCamelCase					     = self.get_dummy_dataset()
													_lowerCamelCase					     = RagRayDistributedRetriever(
													    lowerCamelCase__ ,				question_encoder_tokenizer=self.get_dpr_tokenizer() ,				generator_tokenizer=self.get_bart_tokenizer() ,				retrieval_workers=lowerCamelCase__ ,				)
													if init_retrieval:
																	retriever.init_retrieval()
									return retriever

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__=1_2_3_4_5      ):
									_lowerCamelCase					     = self.get_dummy_dataset()
									_lowerCamelCase					     = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size ,				question_encoder=DPRConfig().to_dict() ,				generator=BartConfig().to_dict() ,				index_name='''custom''' ,				)
									if from_disk:
													_lowerCamelCase					     = os.path.join(self.tmpdirname ,				'''dataset'''      )
													_lowerCamelCase					     = os.path.join(self.tmpdirname ,				'''index.faiss'''      )
													dataset.get_index('''embeddings'''      ).save(os.path.join(self.tmpdirname ,				'''index.faiss'''      )      )
													dataset.drop_index('''embeddings'''      )
													dataset.save_to_disk(os.path.join(self.tmpdirname ,				'''dataset'''      )      )
													del dataset
													_lowerCamelCase					     = RagPyTorchDistributedRetriever(
													    lowerCamelCase__ ,				question_encoder_tokenizer=self.get_dpr_tokenizer() ,				generator_tokenizer=self.get_bart_tokenizer() ,				)
									else:
													_lowerCamelCase					     = RagPyTorchDistributedRetriever(
													    lowerCamelCase__ ,				question_encoder_tokenizer=self.get_dpr_tokenizer() ,				generator_tokenizer=self.get_bart_tokenizer() ,				index=CustomHFIndex(config.retrieval_vector_size ,				lowerCamelCase__      ) ,				)
									if init_retrieval:
													retriever.init_retrieval(lowerCamelCase__      )
									return retriever

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									# Have to run in local mode because sys.path modifications at top of
									# file are not propogated to remote workers.
									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
									ray.init(local_mode=lowerCamelCase__      )
									_lowerCamelCase					     = self.get_dummy_dataset()
									_lowerCamelCase					     = RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size ,				question_encoder=DPRConfig().to_dict() ,				generator=BartConfig().to_dict() ,				index_name='''custom''' ,				)
									_lowerCamelCase					     = ray.remote(lowerCamelCase__      )
									_lowerCamelCase					     = [remote_cls.remote() for _ in range(1      )]
									if from_disk:
													_lowerCamelCase					     = os.path.join(self.tmpdirname ,				'''dataset'''      )
													_lowerCamelCase					     = os.path.join(self.tmpdirname ,				'''index.faiss'''      )
													dataset.get_index('''embeddings'''      ).save(os.path.join(self.tmpdirname ,				'''index.faiss'''      )      )
													dataset.drop_index('''embeddings'''      )
													dataset.save_to_disk(os.path.join(self.tmpdirname ,				'''dataset'''      )      )
													del dataset
													_lowerCamelCase					     = RagRayDistributedRetriever(
													    lowerCamelCase__ ,				question_encoder_tokenizer=self.get_dpr_tokenizer() ,				generator_tokenizer=self.get_bart_tokenizer() ,				retrieval_workers=lowerCamelCase__ ,				index=CustomHFIndex.load_from_disk(
													        vector_size=config.retrieval_vector_size ,				dataset_path=config.passages_path ,				index_path=config.index_path ,				) ,				)
									else:
													_lowerCamelCase					     = RagRayDistributedRetriever(
													    lowerCamelCase__ ,				question_encoder_tokenizer=self.get_dpr_tokenizer() ,				generator_tokenizer=self.get_bart_tokenizer() ,				retrieval_workers=lowerCamelCase__ ,				index=CustomHFIndex(config.retrieval_vector_size ,				lowerCamelCase__      ) ,				)
									if init_retrieval:
													retriever.init_retrieval()
									return retriever

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase     ,   _lowerCamelCase     ,   _lowerCamelCase					     = retriever.retrieve(lowerCamelCase__ ,				n_docs=lowerCamelCase__      )
									self.assertEqual(retrieved_doc_embeds.shape ,				(2, n_docs, self.retrieval_vector_size)      )
									self.assertEqual(len(lowerCamelCase__      ) ,				2      )
									self.assertEqual(sorted(doc_dicts[0]      ) ,				['''embeddings''', '''id''', '''text''', '''title''']      )
									self.assertEqual(len(doc_dicts[0]['''id''']      ) ,				lowerCamelCase__      )
									self.assertEqual(doc_dicts[0]['''id'''][0] ,				'''1'''      )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''id'''][0] ,				'''0'''      )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist() ,				[[1], [0]]      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )] ,				dtype=np.floataa      )

									self.distributed_retriever_check(
									    self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCamelCase__      ) ,				lowerCamelCase__ ,				lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )] ,				dtype=np.floataa      )

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase__ ,				from_disk=lowerCamelCase__      ) ,				lowerCamelCase__ ,				lowerCamelCase__ ,				)

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )] ,				dtype=np.floataa      )

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCamelCase__ ,				from_disk=lowerCamelCase__      ) ,				lowerCamelCase__ ,				lowerCamelCase__ ,				)

					@require_ray
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )] ,				dtype=np.floataa      )

									self.distributed_retriever_check(
									    self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCamelCase__      ) ,				lowerCamelCase__ ,				lowerCamelCase__      )
									ray.shutdown()

					@require_ray
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )] ,				dtype=np.floataa      )
									with self.assertRaises(lowerCamelCase__      ):
													self.distributed_retriever_check(
													    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase__ ,				from_disk=lowerCamelCase__      ) ,				lowerCamelCase__ ,				lowerCamelCase__ ,				)
									ray.shutdown()





					@require_ray
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = 1
									_lowerCamelCase					     = np.array(
									    [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )] ,				dtype=np.floataa      )

									self.distributed_retriever_check(
									    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCamelCase__ ,				from_disk=lowerCamelCase__      ) ,				lowerCamelCase__ ,				lowerCamelCase__      )
									ray.shutdown()





"""simple docstring"""
def 				lowerCAmelCase_(      lowercase_   :							str  ,      lowercase_   :							str							)		->				int:
				# The longer word should come first
				if len(lowercase_							) < len(lowercase_							):
								return levenshtein_distance(lowercase_  ,      lowercase_							)

				if len(lowercase_							) == 0:
								return len(lowercase_							)

				_lowerCamelCase					     = list(range(len(lowercase_							) + 1							)							)

				for i, ca in enumerate(lowercase_							):
								_lowerCamelCase					     = [i + 1]

								for j, ca in enumerate(lowercase_							):
												# Calculate insertions, deletions and substitutions
												_lowerCamelCase					     = previous_row[j + 1] + 1
												_lowerCamelCase					     = current_row[j] + 1
												_lowerCamelCase					     = previous_row[j] + (ca != ca)

												# Get the minimum to append to the current row
												current_row.append(min(lowercase_  ,      lowercase_  ,      lowercase_							)							)

								# Store the previous row
								_lowerCamelCase					     = current_row

				# Returns the last element (distance)
				return previous_row[-1]


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  int										=					input('''Enter the first word:\n''').strip()
			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					input('''Enter the second word:\n''').strip()

			__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					levenshtein_distance(first_word, second_word)
			print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")
