






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:
     UpperCAmelCase__            =	None

if is_ray_available():
     import ray  # noqa: E402 # isort:skip
     from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
     UpperCAmelCase__            =	None
     UpperCAmelCase__            =	None
     UpperCAmelCase__            =	None
def 		_a					(   a				:Optional[Any]				)							->    Dict:
 if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
  a							=   unittest.skip('''test requires Datasets, Faiss, psutil'''				)(a				)
 return test_case






@require_distributed_retrieval
class 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      def    __lowerCAmelCase      (   self   :		Any						)					->Tuple:




       """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    __lowerCAmelCase      (   self   :		Any						)					->DPRQuestionEncoderTokenizer:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		Dict						)					->BartTokenizer:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		List[Any]						)					->List[str]:




       """simple docstring"""


       shutil.rmtree(self.tmpdirname						)







      def    __lowerCAmelCase      (   self   :		Dict						)					->List[Any]:




       """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    __lowerCAmelCase      (   self   :		Optional[int]				,      __UpperCAmelCase   :		bool				,      __UpperCAmelCase   :		Optional[int]=12_345						)					->RagPyTorchDistributedRetriever:




       """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							=   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    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		bool						)					->RagRayDistributedRetriever:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		bool				,      __UpperCAmelCase   :		bool				,      __UpperCAmelCase   :		Any=12_345						)					->Optional[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							=   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    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		bool				,      __UpperCAmelCase   :		bool						)					->Tuple:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		RagRetriever				,      __UpperCAmelCase   :		np.array				,      __UpperCAmelCase   :		int						)					->None:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		Optional[Any]						)					->Optional[Any]:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		Optional[Any]						)					->Any:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		List[Any]						)					->List[Any]:




       """simple docstring"""


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




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		Dict						)					->Optional[Any]:




       """simple docstring"""


       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    __lowerCAmelCase      (   self   :		str						)					->Any:




       """simple docstring"""


       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()










import numpy as np

UpperCAmelCase__            =	[
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]






class 	lowercase_      :






      '''simple docstring'''




      def __init__(   self   :		Optional[Any]						)					->None:




       """simple docstring"""


       a							=   np.array(__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		str						)					->np.ndarray:




       """simple docstring"""


       a						,  a							=   np.where(letter == self.SQUARE						)
       a							=   np.concatenate([indexa + 1, indexa + 1]						)
       return indexes







      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		int				,      __UpperCAmelCase   :		int						)					->str:




       """simple docstring"""


       return self.SQUARE[indexa - 1, indexa - 1]







      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.lower()
       a							=   message.replace('''j'''				,      '''i'''						)

       a							=   ''''''
       for letter_index in range(len(__UpperCAmelCase						)						):
        if message[letter_index] != " ":
         a							=   self.letter_to_numbers(message[letter_index]						)
         a							=   encoded_message + str(numbers[0]						) + str(numbers[1]						)
        elif message[letter_index] == " ":
         a							=   encoded_message + ''' '''

       return encoded_message







      def    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


       a							=   message.replace(''' '''				,      '''  '''						)
       a							=   ''''''
       for numbers_index in range(int(len(__UpperCAmelCase						) / 2						)						):
        if message[numbers_index * 2] != " ":
         a							=   message[numbers_index * 2]
         a							=   message[numbers_index * 2 + 1]

         a							=   self.numbers_to_letter(int(__UpperCAmelCase						)				,      int(__UpperCAmelCase						)						)
         a							=   decoded_message + letter
        elif message[numbers_index * 2] == " ":
         a							=   decoded_message + ''' '''

       return decoded_message


