






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:
       a	: Any      =				None

if is_ray_available():
       import ray  # noqa: E402 # isort:skip
       from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
       a	: Union[str, Any]      =				None
       a	: Optional[Any]      =				None
       a	: Optional[int]      =				None






def        lowerCamelCase__   (					__lowerCamelCase    :  int	)     ->   int:
  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    __UpperCAmelCase							:			Any     =						unittest.skip("""test requires Datasets, Faiss, psutil"""	)(snake_case_	)
  return test_case







@require_distributed_retrieval
class 					a					(				UpperCamelCase_     ):



      """simple docstring"""


      def 			UpperCAmelCase   (   self      :    Optional[Any]      )							->   List[Any]:
        __UpperCAmelCase							:			Dict     =						tempfile.mkdtemp()
        __UpperCAmelCase							:			str     =						8

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

        __UpperCAmelCase							:			Tuple     =						os.path.join(self.tmpdirname	,   """bart_tokenizer"""      )
        os.makedirs(__UpperCamelCase	,   exist_ok=__UpperCamelCase      )
        __UpperCAmelCase							:			int     =						os.path.join(__UpperCamelCase	,   BART_VOCAB_FILES_NAMES["""vocab_file"""]      )
        __UpperCAmelCase							:			List[str]     =						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      :    Union[str, Any]      )							->   DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,   """dpr_tokenizer"""      )      )
      def 			UpperCAmelCase   (   self      :    Any      )							->   BartTokenizer:
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,   """bart_tokenizer"""      )      )
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Optional[int]:
        shutil.rmtree(self.tmpdirname      )
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Union[str, Any]:
        __UpperCAmelCase							:			List[Any]     =						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      :    Optional[int]	,   __lowercase      :    Any	,   __lowercase      :    Optional[Any]=12345      )							->   RagPyTorchDistributedRetriever:
        __UpperCAmelCase							:			Tuple     =						self.get_dummy_dataset()
        __UpperCAmelCase							:			Optional[int]     =						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:
          __UpperCAmelCase							:			Union[str, Any]     =						dataset
          __UpperCAmelCase							:			List[str]     =						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      :    Any	,   __lowercase      :    Dict      )							->   RagRayDistributedRetriever:
        ray.init(local_mode=__UpperCamelCase      )
        __UpperCAmelCase							:			Tuple     =						RagConfig(
            retrieval_vector_size=self.retrieval_vector_size	,   question_encoder=DPRConfig().to_dict()	,   generator=BartConfig().to_dict()	,   )
        __UpperCAmelCase							:			Any     =						ray.remote(__UpperCamelCase      )
        __UpperCAmelCase							:			int     =						[remote_cls.remote() for _ in range(1      )]
        with patch("""transformers.models.rag.retrieval_rag.load_dataset"""      ) as mock_load_dataset:
          __UpperCAmelCase							:			Dict     =						self.get_dummy_dataset()
          __UpperCAmelCase							:			Union[str, Any]     =						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      :    Union[str, Any]	,   __lowercase      :    int	,   __lowercase      :    int	,   __lowercase      :    str=12345      )							->   Optional[Any]:
        __UpperCAmelCase							:			Union[str, Any]     =						self.get_dummy_dataset()
        __UpperCAmelCase							:			Dict     =						RagConfig(
            retrieval_vector_size=self.retrieval_vector_size	,   question_encoder=DPRConfig().to_dict()	,   generator=BartConfig().to_dict()	,   index_name="""custom"""	,   )
        if from_disk:
          __UpperCAmelCase							:			List[Any]     =						os.path.join(self.tmpdirname	,   """dataset"""      )
          __UpperCAmelCase							:			List[str]     =						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
          __UpperCAmelCase							:			Any     =						RagPyTorchDistributedRetriever(
              __UpperCamelCase	,   question_encoder_tokenizer=self.get_dpr_tokenizer()	,   generator_tokenizer=self.get_bart_tokenizer()	,   )
        else:
          __UpperCAmelCase							:			Tuple     =						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      :    Dict	,   __lowercase      :    Any	,   __lowercase      :    Dict      )							->   Union[str, Any]:
        ray.init(local_mode=__UpperCamelCase      )
        __UpperCAmelCase							:			int     =						self.get_dummy_dataset()
        __UpperCAmelCase							:			Optional[int]     =						RagConfig(
            retrieval_vector_size=self.retrieval_vector_size	,   question_encoder=DPRConfig().to_dict()	,   generator=BartConfig().to_dict()	,   index_name="""custom"""	,   )
        __UpperCAmelCase							:			Any     =						ray.remote(__UpperCamelCase      )
        __UpperCAmelCase							:			Optional[int]     =						[remote_cls.remote() for _ in range(1      )]
        if from_disk:
          __UpperCAmelCase							:			Optional[int]     =						os.path.join(self.tmpdirname	,   """dataset"""      )
          __UpperCAmelCase							:			Tuple     =						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
          __UpperCAmelCase							:			Dict     =						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:
          __UpperCAmelCase							:			Union[str, Any]     =						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      :    Optional[Any]	,   __lowercase      :    Tuple	,   __lowercase      :    Optional[int]	,   __lowercase      :    Dict      )							->   None:
        __UpperCAmelCase							:			str     =						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      :    Optional[int]      )							->   Dict:
        __UpperCAmelCase							:			Optional[int]     =						1
        __UpperCAmelCase							:			List[str]     =						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      :    Optional[int]      )							->   Union[str, Any]:
        __UpperCAmelCase							:			str     =						1
        __UpperCAmelCase							:			Optional[int]     =						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      :    str      )							->   List[str]:
        __UpperCAmelCase							:			Optional[int]     =						1
        __UpperCAmelCase							:			Union[str, Any]     =						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      :    Dict      )							->   Dict:
        __UpperCAmelCase							:			Tuple     =						1
        __UpperCAmelCase							:			List[str]     =						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      :    Dict      )							->   Tuple:
        __UpperCAmelCase							:			str     =						1
        __UpperCAmelCase							:			Optional[Any]     =						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      :    int      )							->   Union[str, Any]:
        __UpperCAmelCase							:			Union[str, Any]     =						1
        __UpperCAmelCase							:			List[str]     =						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()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a	: Optional[int]      =				{
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: Optional[int]      =				["DeformableDetrFeatureExtractor"]
       a	: List[str]      =				["DeformableDetrImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[Any]      =				[
           "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DeformableDetrForObjectDetection",
           "DeformableDetrModel",
           "DeformableDetrPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
              from .image_processing_deformable_detr import DeformableDetrImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deformable_detr import (
                  DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DeformableDetrForObjectDetection,
                  DeformableDetrModel,
                  DeformableDetrPreTrainedModel,
              )

else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


