





'''simple docstring'''

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:
      lowercase_         =			None

if is_ray_available():
      import ray  # noqa: E402 # isort:skip
      from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
      lowercase_         =			None
      lowercase_         =			None
      lowercase_         =			None





def lowerCamelCase						(					__lowerCamelCase			:   Optional[Any]   )						->Any:
    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        _SCREAMING_SNAKE_CASE     =      unittest.skip("""test requires Datasets, Faiss, psutil"""   )(__lowerCamelCase   )
    return test_case






@require_distributed_retrieval
class  a_					(    snake_case_	):

    '''simple docstring'''


    def       snake_case_(		self	)		->       List[Any]:
        _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(A			,		exist_ok=A	)
        _SCREAMING_SNAKE_CASE     =      os.path.join(A			,		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(A			,		range(len(A	)	)	)	)
        _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(A			,		exist_ok=A	)
        _SCREAMING_SNAKE_CASE     =      os.path.join(A			,		BART_VOCAB_FILES_NAMES["""vocab_file"""]	)
        _SCREAMING_SNAKE_CASE     =      os.path.join(A			,		BART_VOCAB_FILES_NAMES["""merges_file"""]	)
        with open(self.vocab_file			,		"""w"""			,		encoding="""utf-8"""	) as fp:
            fp.write(json.dumps(A	) + """\n"""	)
        with open(self.merges_file			,		"""w"""			,		encoding="""utf-8"""	) as fp:
            fp.write("""\n""".join(A	)	)


    def       snake_case_(		self	)		->       DPRQuestionEncoderTokenizer:
        return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,		"""dpr_tokenizer"""	)	)


    def       snake_case_(		self	)		->       BartTokenizer:
        return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,		"""bart_tokenizer"""	)	)


    def       snake_case_(		self	)		->       Optional[int]:
        shutil.rmtree(self.tmpdirname	)


    def       snake_case_(		self	)		->       List[str]:
        _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       snake_case_(		self			,		A			,		A=1_2345	)		->       RagPyTorchDistributedRetriever:
        _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(
                A			,		question_encoder_tokenizer=self.get_dpr_tokenizer()			,		generator_tokenizer=self.get_bart_tokenizer()			,		)
            if init_retrieval:
                retriever.init_retrieval(A	)
        return retriever


    def       snake_case_(		self			,		A	)		->       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=A	)
        _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(A	)
        _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(
                A			,		question_encoder_tokenizer=self.get_dpr_tokenizer()			,		generator_tokenizer=self.get_bart_tokenizer()			,		retrieval_workers=A			,		)
            if init_retrieval:
                retriever.init_retrieval()
        return retriever


    def       snake_case_(		self			,		A			,		A			,		A=1_2345	)		->       List[Any]:
        _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(
                A			,		question_encoder_tokenizer=self.get_dpr_tokenizer()			,		generator_tokenizer=self.get_bart_tokenizer()			,		)
        else:
            _SCREAMING_SNAKE_CASE     =      RagPyTorchDistributedRetriever(
                A			,		question_encoder_tokenizer=self.get_dpr_tokenizer()			,		generator_tokenizer=self.get_bart_tokenizer()			,		index=CustomHFIndex(config.retrieval_vector_size			,		A	)			,		)
        if init_retrieval:
            retriever.init_retrieval(A	)
        return retriever


    def       snake_case_(		self			,		A			,		A	)		->       Any:
        # 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=A	)
        _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(A	)
        _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(
                A			,		question_encoder_tokenizer=self.get_dpr_tokenizer()			,		generator_tokenizer=self.get_bart_tokenizer()			,		retrieval_workers=A			,		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(
                A			,		question_encoder_tokenizer=self.get_dpr_tokenizer()			,		generator_tokenizer=self.get_bart_tokenizer()			,		retrieval_workers=A			,		index=CustomHFIndex(config.retrieval_vector_size			,		A	)			,		)
        if init_retrieval:
            retriever.init_retrieval()
        return retriever


    def       snake_case_(		self			,		A			,		A			,		A	)		->       None:
        _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE     =      retriever.retrieve(A			,		n_docs=A	)
        self.assertEqual(retrieved_doc_embeds.shape			,		(2, n_docs, self.retrieval_vector_size)	)
        self.assertEqual(len(A	)			,		2	)
        self.assertEqual(sorted(doc_dicts[0]	)			,		["""embeddings""", """id""", """text""", """title"""]	)
        self.assertEqual(len(doc_dicts[0]["""id"""]	)			,		A	)
        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       snake_case_(		self	)		->       List[str]:
        _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=A	)			,		A			,		A	)


    def       snake_case_(		self	)		->       List[Any]:
        _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=A			,		from_disk=A	)			,		A			,		A			,		)


    def       snake_case_(		self	)		->       Tuple:
        _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=A			,		from_disk=A	)			,		A			,		A			,		)


    @require_ray
    def       snake_case_(		self	)		->       Union[str, Any]:
        _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=A	)			,		A			,		A	)
        ray.shutdown()


    @require_ray
    def       snake_case_(		self	)		->       Tuple:
        _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(A	):
            self.distributed_retriever_check(
                self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=A			,		from_disk=A	)			,		A			,		A			,		)
        ray.shutdown()





    @require_ray
    def       snake_case_(		self	)		->       int:
        _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=A			,		from_disk=A	)			,		A			,		A	)
        ray.shutdown()










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _SCREAMING_SNAKE_CASE     =      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



