


"""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:
   __snake_case												=							None

if is_ray_available():
   import ray  # noqa: E402 # isort:skip
   from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
   __snake_case												=							None
   __snake_case												=							None
   __snake_case												=							None
def    A_ ( _lowerCAmelCase  :       int							):






     """simple docstring"""

     if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
          _a     						=					unittest.skip('''test requires Datasets, Faiss, psutil'''							)(_lowerCAmelCase							)
     return test_case


@require_distributed_retrieval
class      __lowerCamelCase	(       a__					):

     '''simple docstring'''


     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          _a     						=					tempfile.mkdtemp()
          _a     						=					8

          # DPR tok
          _a     						=					[
              '''[UNK]''',
              '''[CLS]''',
              '''[SEP]''',
              '''[PAD]''',
              '''[MASK]''',
              '''want''',
              '''##want''',
              '''##ed''',
              '''wa''',
              '''un''',
              '''runn''',
              '''##ing''',
              ''',''',
              '''low''',
              '''lowest''',
          ]
          _a     						=					os.path.join(self.tmpdirname     ,					'''dpr_tokenizer'''				)
          os.makedirs(__UpperCAmelCase     ,					exist_ok=__UpperCAmelCase				)
          _a     						=					os.path.join(__UpperCAmelCase     ,					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
          _a     						=					[
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''\u0120''',
              '''\u0120l''',
              '''\u0120n''',
              '''\u0120lo''',
              '''\u0120low''',
              '''er''',
              '''\u0120lowest''',
              '''\u0120newer''',
              '''\u0120wider''',
              '''<unk>''',
          ]
          _a     						=					dict(zip(__UpperCAmelCase     ,					range(len(__UpperCAmelCase				)				)				)				)
          _a     						=					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
          _a     						=					{'''unk_token''': '''<unk>'''}

          _a     						=					os.path.join(self.tmpdirname     ,					'''bart_tokenizer'''				)
          os.makedirs(__UpperCAmelCase     ,					exist_ok=__UpperCAmelCase				)
          _a     						=					os.path.join(__UpperCAmelCase     ,					BART_VOCAB_FILES_NAMES['''vocab_file''']				)
          _a     						=					os.path.join(__UpperCAmelCase     ,					BART_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 				_UpperCAmelCase   (   self				)							->				DPRQuestionEncoderTokenizer:
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,					'''dpr_tokenizer'''				)				)


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


     def 				_UpperCAmelCase   (   self				)							->				Optional[int]:
          shutil.rmtree(self.tmpdirname				)


     def 				_UpperCAmelCase   (   self				)							->				Any:
          _a     						=					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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=12345				)							->				RagPyTorchDistributedRetriever:
          _a     						=					self.get_dummy_dataset()
          _a     						=					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:
               _a     						=					dataset
               _a     						=					RagPyTorchDistributedRetriever(
                   __UpperCAmelCase     ,					question_encoder_tokenizer=self.get_dpr_tokenizer()     ,					generator_tokenizer=self.get_bart_tokenizer()     ,					)
               if init_retrieval:
                    retriever.init_retrieval(__UpperCAmelCase				)
          return retriever


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				RagRayDistributedRetriever:
          # 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=__UpperCAmelCase				)
          _a     						=					RagConfig(
              retrieval_vector_size=self.retrieval_vector_size     ,					question_encoder=DPRConfig().to_dict()     ,					generator=BartConfig().to_dict()     ,					)
          _a     						=					ray.remote(__UpperCAmelCase				)
          _a     						=					[remote_cls.remote() for _ in range(1				)]
          with patch('''transformers.models.rag.retrieval_rag.load_dataset'''				) as mock_load_dataset:
               _a     						=					self.get_dummy_dataset()
               _a     						=					RagRayDistributedRetriever(
                   __UpperCAmelCase     ,					question_encoder_tokenizer=self.get_dpr_tokenizer()     ,					generator_tokenizer=self.get_bart_tokenizer()     ,					retrieval_workers=__UpperCAmelCase     ,					)
               if init_retrieval:
                    retriever.init_retrieval()
          return retriever


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=12345				)							->				Any:
          _a     						=					self.get_dummy_dataset()
          _a     						=					RagConfig(
              retrieval_vector_size=self.retrieval_vector_size     ,					question_encoder=DPRConfig().to_dict()     ,					generator=BartConfig().to_dict()     ,					index_name='''custom'''     ,					)
          if from_disk:
               _a     						=					os.path.join(self.tmpdirname     ,					'''dataset'''				)
               _a     						=					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
               _a     						=					RagPyTorchDistributedRetriever(
                   __UpperCAmelCase     ,					question_encoder_tokenizer=self.get_dpr_tokenizer()     ,					generator_tokenizer=self.get_bart_tokenizer()     ,					)
          else:
               _a     						=					RagPyTorchDistributedRetriever(
                   __UpperCAmelCase     ,					question_encoder_tokenizer=self.get_dpr_tokenizer()     ,					generator_tokenizer=self.get_bart_tokenizer()     ,					index=CustomHFIndex(config.retrieval_vector_size     ,					__UpperCAmelCase				)     ,					)
          if init_retrieval:
               retriever.init_retrieval(__UpperCAmelCase				)
          return retriever


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				Tuple:
          # 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=__UpperCAmelCase				)
          _a     						=					self.get_dummy_dataset()
          _a     						=					RagConfig(
              retrieval_vector_size=self.retrieval_vector_size     ,					question_encoder=DPRConfig().to_dict()     ,					generator=BartConfig().to_dict()     ,					index_name='''custom'''     ,					)
          _a     						=					ray.remote(__UpperCAmelCase				)
          _a     						=					[remote_cls.remote() for _ in range(1				)]
          if from_disk:
               _a     						=					os.path.join(self.tmpdirname     ,					'''dataset'''				)
               _a     						=					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
               _a     						=					RagRayDistributedRetriever(
                   __UpperCAmelCase     ,					question_encoder_tokenizer=self.get_dpr_tokenizer()     ,					generator_tokenizer=self.get_bart_tokenizer()     ,					retrieval_workers=__UpperCAmelCase     ,					index=CustomHFIndex.load_from_disk(
                       vector_size=config.retrieval_vector_size     ,					dataset_path=config.passages_path     ,					index_path=config.index_path     ,					)     ,					)
          else:
               _a     						=					RagRayDistributedRetriever(
                   __UpperCAmelCase     ,					question_encoder_tokenizer=self.get_dpr_tokenizer()     ,					generator_tokenizer=self.get_bart_tokenizer()     ,					retrieval_workers=__UpperCAmelCase     ,					index=CustomHFIndex(config.retrieval_vector_size     ,					__UpperCAmelCase				)     ,					)
          if init_retrieval:
               retriever.init_retrieval()
          return retriever


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				None:
          _a						,   _a						,   _a     						=					retriever.retrieve(__UpperCAmelCase     ,					n_docs=__UpperCAmelCase				)
          self.assertEqual(retrieved_doc_embeds.shape     ,					(2, n_docs, self.retrieval_vector_size)				)
          self.assertEqual(len(__UpperCAmelCase				)     ,					2				)
          self.assertEqual(sorted(doc_dicts[0]				)     ,					['''embeddings''', '''id''', '''text''', '''title''']				)
          self.assertEqual(len(doc_dicts[0]['''id''']				)     ,					__UpperCAmelCase				)
          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 				_UpperCAmelCase   (   self				)							->				List[str]:
          _a     						=					1
          _a     						=					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=__UpperCAmelCase				)     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)


     def 				_UpperCAmelCase   (   self				)							->				str:
          _a     						=					1
          _a     						=					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=__UpperCAmelCase     ,					from_disk=__UpperCAmelCase				)     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					1
          _a     						=					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=__UpperCAmelCase     ,					from_disk=__UpperCAmelCase				)     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)


     @require_ray
     def 				_UpperCAmelCase   (   self				)							->				List[Any]:
          _a     						=					1
          _a     						=					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=__UpperCAmelCase				)     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
          ray.shutdown()


     @require_ray
     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					1
          _a     						=					np.array(
              [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]     ,					dtype=np.floataa				)
          with self.assertRaises(__UpperCAmelCase				):
               self.distributed_retriever_check(
                   self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=__UpperCAmelCase     ,					from_disk=__UpperCAmelCase				)     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)
          ray.shutdown()





     @require_ray
     def 				_UpperCAmelCase   (   self				)							->				Tuple:
          _a     						=					1
          _a     						=					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=__UpperCAmelCase     ,					from_disk=__UpperCAmelCase				)     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)
          ray.shutdown()



"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

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

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

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

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

          # Store the previous row
          _a     						=					current_row

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


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')