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:
   __lowerCamelCase     :					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:
   __lowerCamelCase     :					str				  =						None
   __lowerCamelCase     :					Tuple				  =						None
   __lowerCamelCase     :					Optional[int]				  =						None



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Optional[int]      )				->   Optional[int]:







  """simple docstring"""





  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    SCREAMING_SNAKE_CASE__     				= unittest.skip("""test requires Datasets, Faiss, psutil"""      )(a_      )
  return test_case







@require_distributed_retrieval
class __snake_case							(		UpperCamelCase__						):




   def       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE__     				= 8

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

     SCREAMING_SNAKE_CASE__     				= os.path.join(self.tmpdirname			,      """bart_tokenizer""" )
     os.makedirs(_UpperCAmelCase			,      exist_ok=_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= os.path.join(_UpperCAmelCase			,      BART_VOCAB_FILES_NAMES["""vocab_file"""] )
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Optional[int] ):



     """simple docstring"""






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




   def       __a		(       self     :							str ):



     """simple docstring"""






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




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     shutil.rmtree(self.tmpdirname )




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Optional[int]			,      _lowercase     :							Any			,      _lowercase     :							List[Any]=1_23_45 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_dummy_dataset()
     SCREAMING_SNAKE_CASE__     				= 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:
       SCREAMING_SNAKE_CASE__     				= dataset
       SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							str			,      _lowercase     :							Dict ):



     """simple docstring"""






     ray.init(local_mode=_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= RagConfig(
         retrieval_vector_size=self.retrieval_vector_size			,      question_encoder=DPRConfig().to_dict()			,      generator=BartConfig().to_dict()			,      )
     SCREAMING_SNAKE_CASE__     				= ray.remote(_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= [remote_cls.remote() for _ in range(1 )]
     with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
       SCREAMING_SNAKE_CASE__     				= self.get_dummy_dataset()
       SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Tuple			,      _lowercase     :							List[Any]			,      _lowercase     :							Tuple			,      _lowercase     :							Optional[int]=1_23_45 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.get_dummy_dataset()
     SCREAMING_SNAKE_CASE__     				= RagConfig(
         retrieval_vector_size=self.retrieval_vector_size			,      question_encoder=DPRConfig().to_dict()			,      generator=BartConfig().to_dict()			,      index_name="""custom"""			,      )
     if from_disk:
       SCREAMING_SNAKE_CASE__     				= os.path.join(self.tmpdirname			,      """dataset""" )
       SCREAMING_SNAKE_CASE__     				= 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
       SCREAMING_SNAKE_CASE__     				= RagPyTorchDistributedRetriever(
           _UpperCAmelCase			,      question_encoder_tokenizer=self.get_dpr_tokenizer()			,      generator_tokenizer=self.get_bart_tokenizer()			,      )
     else:
       SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Dict			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							str ):



     """simple docstring"""






     ray.init(local_mode=_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_dataset()
     SCREAMING_SNAKE_CASE__     				= RagConfig(
         retrieval_vector_size=self.retrieval_vector_size			,      question_encoder=DPRConfig().to_dict()			,      generator=BartConfig().to_dict()			,      index_name="""custom"""			,      )
     SCREAMING_SNAKE_CASE__     				= ray.remote(_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= [remote_cls.remote() for _ in range(1 )]
     if from_disk:
       SCREAMING_SNAKE_CASE__     				= os.path.join(self.tmpdirname			,      """dataset""" )
       SCREAMING_SNAKE_CASE__     				= 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
       SCREAMING_SNAKE_CASE__     				= 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:
       SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Optional[Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							List[str]			,      _lowercase     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Any ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= 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       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= 1
     SCREAMING_SNAKE_CASE__     				= 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'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  import torch

  from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class 					__UpperCAmelCase (		unittest.TestCase	):

      '''simple docstring'''





      @slow
      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   Optional[int]												=	CamembertModel.from_pretrained('''camembert-base'''						)
             model.to(_UpperCAmelCase						)

             UpperCAmelCase__	:   Optional[int]												=	torch.tensor(
                 [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,		device=_UpperCAmelCase  ,		dtype=torch.long  ,		)  # J'aime le camembert !
             with torch.no_grad():
                    UpperCAmelCase__	:   Tuple												=	model(_UpperCAmelCase						)['''last_hidden_state''']
             UpperCAmelCase__	:   List[str]												=	torch.Size((1, 10, 768)						)
             self.assertEqual(output.shape  ,		_UpperCAmelCase						)
             # compare the actual values for a slice.
             UpperCAmelCase__	:   List[Any]												=	torch.tensor(
                 [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]  ,		device=_UpperCAmelCase  ,		dtype=torch.float  ,		)
             # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
             # camembert.eval()
             # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

             self.assertTrue(torch.allclose(output[:, :3, :3]  ,		_UpperCAmelCase  ,		atol=1E-4						)						)