




import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
  import faiss


@require_faiss
class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->DPRQuestionEncoderTokenizer:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->DPRContextEncoderTokenizer:





           '''simple docstring'''

           return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname      ,				'''dpr_tokenizer'''))


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->BartTokenizer:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[int]:





           '''simple docstring'''

           shutil.rmtree(self.tmpdirname)


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





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->List[Any]:





           '''simple docstring'''

           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__   				=					RagRetriever(
                      UpperCAmelCase__      ,				question_encoder_tokenizer=self.get_dpr_tokenizer()      ,				generator_tokenizer=self.get_bart_tokenizer()      ,				)
           return retriever


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			bool)		->int:





           '''simple docstring'''

           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__   				=					RagRetriever(
                      UpperCAmelCase__      ,				question_encoder_tokenizer=self.get_dpr_tokenizer()      ,				generator_tokenizer=self.get_bart_tokenizer()      ,				)
           else:
                  A__   				=					RagRetriever(
                      UpperCAmelCase__      ,				question_encoder_tokenizer=self.get_dpr_tokenizer()      ,				generator_tokenizer=self.get_bart_tokenizer()      ,				index=CustomHFIndex(config.retrieval_vector_size      ,				UpperCAmelCase__)      ,				)
           return retriever


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

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

           A__   				=					os.path.join(self.tmpdirname      ,				'''hf_bert_base.hnswSQ8_correct_phi_128.c_index''')
           dataset.save_faiss_index('''embeddings'''      ,				index_file_name + '''.index.dpr''')
           pickle.dump(dataset['''id''']      ,				open(index_file_name + '''.index_meta.dpr'''      ,				'''wb'''))

           A__   				=					os.path.join(self.tmpdirname      ,				'''psgs_w100.tsv.pkl''')
           A__   				=					{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
           pickle.dump(UpperCAmelCase__      ,				open(UpperCAmelCase__      ,				'''wb'''))

           A__   				=					RagConfig(
               retrieval_vector_size=self.retrieval_vector_size      ,				question_encoder=DPRConfig().to_dict()      ,				generator=BartConfig().to_dict()      ,				index_name='''legacy'''      ,				index_path=self.tmpdirname      ,				)
           A__   				=					RagRetriever(
               UpperCAmelCase__      ,				question_encoder_tokenizer=self.get_dpr_tokenizer()      ,				generator_tokenizer=self.get_bart_tokenizer())
           return retriever


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->List[str]:





           '''simple docstring'''

           A__   				=					1
           A__   				=					self.get_dummy_canonical_hf_index_retriever()
           A__   				=					np.array(
               [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
           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  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Dict:





           '''simple docstring'''

           A__   				=					self.get_dummy_canonical_hf_index_retriever()
           with tempfile.TemporaryDirectory() as tmp_dirname:
                  with patch('''transformers.models.rag.retrieval_rag.load_dataset''') as mock_load_dataset:
                         A__   				=					self.get_dummy_dataset()
                         retriever.save_pretrained(UpperCAmelCase__)
                         A__   				=					RagRetriever.from_pretrained(UpperCAmelCase__)
                         self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)
                         A__   				=					np.array(
                             [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
                         A__   				=					retriever.retrieve(UpperCAmelCase__      ,				n_docs=1)
                         self.assertTrue(out is not None)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->int:





           '''simple docstring'''

           A__   				=					1
           A__   				=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__)
           A__   				=					np.array(
               [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
           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  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Tuple:





           '''simple docstring'''

           A__   				=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                  retriever.save_pretrained(UpperCAmelCase__)
                  A__   				=					RagRetriever.from_pretrained(UpperCAmelCase__)
                  self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)
                  A__   				=					np.array(
                      [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
                  A__   				=					retriever.retrieve(UpperCAmelCase__      ,				n_docs=1)
                  self.assertTrue(out is not None)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					1
           A__   				=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__)
           A__   				=					np.array(
               [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
           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  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__)
           with tempfile.TemporaryDirectory() as tmp_dirname:
                  retriever.save_pretrained(UpperCAmelCase__)
                  A__   				=					RagRetriever.from_pretrained(UpperCAmelCase__)
                  self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)
                  A__   				=					np.array(
                      [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
                  A__   				=					retriever.retrieve(UpperCAmelCase__      ,				n_docs=1)
                  self.assertTrue(out is not None)


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





           '''simple docstring'''

           A__   				=					1
           A__   				=					self.get_dummy_legacy_index_retriever()
           A__   				=					np.array(
               [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
           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])      ,				['''text''', '''title'''])
           self.assertEqual(len(doc_dicts[0]['''text'''])      ,				UpperCAmelCase__)
           self.assertEqual(doc_dicts[0]['''text'''][0]      ,				'''bar''')  # max inner product is reached with second doc
           self.assertEqual(doc_dicts[1]['''text'''][0]      ,				'''foo''')  # max inner product is reached with first doc
           self.assertListEqual(doc_ids.tolist()      ,				[[1], [0]])


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Tuple:





           '''simple docstring'''

           A__   				=					self.get_dummy_legacy_index_retriever()
           with tempfile.TemporaryDirectory() as tmp_dirname:
                  retriever.save_pretrained(UpperCAmelCase__)
                  A__   				=					RagRetriever.from_pretrained(UpperCAmelCase__)
                  self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)
                  A__   				=					np.array(
                      [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
                  A__   				=					retriever.retrieve(UpperCAmelCase__      ,				n_docs=1)
                  self.assertTrue(out is not None)


    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->List[str]:





           '''simple docstring'''

           import torch

           A__   				=					1
           A__   				=					self.get_dummy_canonical_hf_index_retriever()
           A__   				=					[[5, 7], [10, 11]]
           A__   				=					np.array(
               [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
           A__   				=					retriever(UpperCAmelCase__      ,				UpperCAmelCase__      ,				prefix=retriever.config.generator.prefix      ,				n_docs=UpperCAmelCase__)
           A__				,							A__				,							A__   				=					(
               out['''context_input_ids'''],
               out['''context_attention_mask'''],
               out['''retrieved_doc_embeds'''],
           )
           self.assertEqual(retrieved_doc_embeds.shape      ,				(2, n_docs, self.retrieval_vector_size))
           self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)
           self.assertIsInstance(UpperCAmelCase__      ,				UpperCAmelCase__)
           self.assertIsInstance(UpperCAmelCase__      ,				np.ndarray)

           A__   				=					retriever(
               UpperCAmelCase__      ,				UpperCAmelCase__      ,				prefix=retriever.config.generator.prefix      ,				n_docs=UpperCAmelCase__      ,				return_tensors='''pt'''      ,				)
           A__				,							A__				,							A__				,							A__   				=					(  # noqa: F841
               out['''context_input_ids'''],
               out['''context_attention_mask'''],
               out['''retrieved_doc_embeds'''],
               out['''doc_ids'''],
           )
           self.assertEqual(retrieved_doc_embeds.shape      ,				(2, n_docs, self.retrieval_vector_size))
           self.assertIsInstance(UpperCAmelCase__      ,				torch.Tensor)
           self.assertIsInstance(UpperCAmelCase__      ,				torch.Tensor)
           self.assertIsInstance(UpperCAmelCase__      ,				torch.Tensor)





    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->str:





           '''simple docstring'''

           A__   				=					self.get_dpr_ctx_encoder_tokenizer()
           A__   				=					1
           A__   				=					self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__)
           retriever.set_ctx_encoder_tokenizer(UpperCAmelCase__)

           A__   				=					[[5, 7], [10, 11]]
           A__   				=					np.array(
               [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]      ,				dtype=np.floataa)
           A__   				=					retriever(UpperCAmelCase__      ,				UpperCAmelCase__      ,				prefix=retriever.config.generator.prefix      ,				n_docs=UpperCAmelCase__)

           self.assertEqual(
               len(UpperCAmelCase__)      ,				6)  # check whether the retriever output consist of 6 attributes including tokenized docs
           self.assertEqual(
               all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask'''))      ,				UpperCAmelCase__)  # check for doc token related keys in dictionary.








def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> float:



       """simple docstring"""

       def get_matched_characters(lowercase_				,       lowercase_		) -> str:
              A__   				=					[]
              A__   				=					min(len(_stra		)				,       len(_stra		)		) // 2
              for i, l in enumerate(_stra		):
                     A__   				=					int(max(0				,       i - limit		)		)
                     A__   				=					int(min(i + limit + 1				,       len(_stra		)		)		)
                     if l in _stra[left:right]:
                            matched.append(lowercase_		)
                            A__   				=					f"""{_stra[0:_stra.index(lowercase_		)]} {_stra[_stra.index(lowercase_		) + 1:]}"""

              return "".join(lowercase_		)

       # matching characters
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					len(lowercase_		)

       # transposition
       A__   				=					(
           len([(ca, ca) for ca, ca in zip(lowercase_				,       lowercase_		) if ca != ca]		) // 2
       )

       if not match_count:
              A__   				=					0.0
       else:
              A__   				=					(
                  1
                  / 3
                  * (
                      match_count / len(lowercase_		)
                      + match_count / len(lowercase_		)
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       A__   				=					0
       for ca, ca in zip(stra[:4]				,       stra[:4]		):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))


