


from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


lowerCamelCase		=						{
    'configuration_ernie_m': ['ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieMConfig'],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=						['ErnieMTokenizer']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=						[
		    'ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ErnieMForMultipleChoice',
		    'ErnieMForQuestionAnswering',
		    'ErnieMForSequenceClassification',
		    'ErnieMForTokenClassification',
		    'ErnieMModel',
		    'ErnieMPreTrainedModel',
		    'ErnieMForInformationExtraction',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase		=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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:					List[Any]     =None

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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]    ):






  """simple docstring"""




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





@require_distributed_retrieval
class 						__lowercase(     SCREAMING_SNAKE_CASE   ):


       """simple docstring"""







       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[Any]:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   DPRQuestionEncoderTokenizer:
         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname  ,      'dpr_tokenizer'       )       )



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



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   int:
         shutil.rmtree(self.tmpdirname       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   str:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			int  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			Tuple=1_2345       )					->   RagPyTorchDistributedRetriever:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Any  ,      _lowerCAmelCase					:			bool       )					->   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=_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 SCREAMING_SNAKE_CASE_				(     self					:			Optional[Any]  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			List[Any]=1_2345       )					->   List[Any]:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]  ,      _lowerCAmelCase					:			bool  ,      _lowerCAmelCase					:			bool       )					->   int:
         # 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 SCREAMING_SNAKE_CASE_				(     self					:			str  ,      _lowerCAmelCase					:			RagRetriever  ,      _lowerCAmelCase					:			np.array  ,      _lowerCAmelCase					:			int       )					->   None:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Optional[int]:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   Any:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			int       )					->   Optional[Any]:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Any:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   Optional[Any]:
         _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 SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   List[Any]:
         _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()
