"""simple docstring"""




import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
     import faiss
@require_faiss
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       List[Any]  			=     8

       # DPR tok
       _lowerCAmelCase						:       str  			=     [
           """[UNK]""",
           """[CLS]""",
           """[SEP]""",
           """[PAD]""",
           """[MASK]""",
           """want""",
           """##want""",
           """##ed""",
           """wa""",
           """un""",
           """runn""",
           """##ing""",
           """,""",
           """low""",
           """lowest""",
       ]
       _lowerCAmelCase						:       List[str]  			=     os.path.join(self.tmpdirname				,     """dpr_tokenizer"""  )
       os.makedirs(a__				,     exist_ok=a__  )
       _lowerCAmelCase						:       int  			=     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
       _lowerCAmelCase						:       int  			=     [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
       ]
       _lowerCAmelCase						:       str  			=     dict(zip(a__				,     range(len(a__  )  )  )  )
       _lowerCAmelCase						:       Optional[Any]  			=     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       _lowerCAmelCase						:       List[str]  			=     {"""unk_token""": """<unk>"""}

       _lowerCAmelCase						:       Dict  			=     os.path.join(self.tmpdirname				,     """bart_tokenizer"""  )
       os.makedirs(a__				,     exist_ok=a__  )
       _lowerCAmelCase						:       Dict  			=     os.path.join(a__				,     BART_VOCAB_FILES_NAMES["""vocab_file"""]  )
       _lowerCAmelCase						:       Tuple  			=     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     __A  (		self  ):
       return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     """dpr_tokenizer"""  )  )






     def     __A  (		self  ):
       return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     """dpr_tokenizer"""  )  )






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






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[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     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     self.get_dummy_dataset()
       _lowerCAmelCase						:       Optional[Any]  			=     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						:       List[Any]  			=     dataset
         _lowerCAmelCase						:       Optional[Any]  			=     RagRetriever(
             a__				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()				,     )
       return retriever






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_dummy_dataset()
       _lowerCAmelCase						:       Union[str, Any]  			=     RagConfig(
           retrieval_vector_size=self.retrieval_vector_size				,     question_encoder=DPRConfig().to_dict()				,     generator=BartConfig().to_dict()				,     index_name="""custom"""				,     )
       if from_disk:
         _lowerCAmelCase						:       Optional[int]  			=     os.path.join(self.tmpdirname				,     """dataset"""  )
         _lowerCAmelCase						:       List[Any]  			=     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						:       Optional[int]  			=     RagRetriever(
             a__				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()				,     )
       else:
         _lowerCAmelCase						:       int  			=     RagRetriever(
             a__				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()				,     index=CustomHFIndex(config.retrieval_vector_size				,     a__  )				,     )
       return retriever






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     Dataset.from_dict(
           {
               """id""": ["""0""", """1"""],
               """text""": ["""foo""", """bar"""],
               """title""": ["""Foo""", """Bar"""],
               """embeddings""": [np.ones(self.retrieval_vector_size + 1  ), 2 * np.ones(self.retrieval_vector_size + 1  )],
           }  )
       dataset.add_faiss_index("""embeddings"""				,     string_factory="""Flat"""				,     metric_type=faiss.METRIC_INNER_PRODUCT  )

       _lowerCAmelCase						:       Optional[Any]  			=     os.path.join(self.tmpdirname				,     """hf_bert_base.hnswSQ8_correct_phi_128.c_index"""  )
       dataset.save_faiss_index("""embeddings"""				,     index_file_name + """.index.dpr"""  )
       pickle.dump(dataset["""id"""]				,     open(index_file_name + """.index_meta.dpr"""				,     """wb"""  )  )

       _lowerCAmelCase						:       Union[str, Any]  			=     os.path.join(self.tmpdirname				,     """psgs_w100.tsv.pkl"""  )
       _lowerCAmelCase						:       Union[str, Any]  			=     {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
       pickle.dump(a__				,     open(a__				,     """wb"""  )  )

       _lowerCAmelCase						:       List[str]  			=     RagConfig(
           retrieval_vector_size=self.retrieval_vector_size				,     question_encoder=DPRConfig().to_dict()				,     generator=BartConfig().to_dict()				,     index_name="""legacy"""				,     index_path=self.tmpdirname				,     )
       _lowerCAmelCase						:       str  			=     RagRetriever(
           a__				,     question_encoder_tokenizer=self.get_dpr_tokenizer()				,     generator_tokenizer=self.get_bart_tokenizer()  )
       return retriever






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     1
       _lowerCAmelCase						:       Dict  			=     self.get_dummy_canonical_hf_index_retriever()
       _lowerCAmelCase						:       int  			=     np.array(
           [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     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     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_dummy_canonical_hf_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
         with patch("""transformers.models.rag.retrieval_rag.load_dataset"""  ) as mock_load_dataset:
           _lowerCAmelCase						:       Optional[int]  			=     self.get_dummy_dataset()
           retriever.save_pretrained(a__  )
           _lowerCAmelCase						:       Dict  			=     RagRetriever.from_pretrained(a__  )
           self.assertIsInstance(a__				,     a__  )
           _lowerCAmelCase						:       Any  			=     np.array(
               [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
           _lowerCAmelCase						:       int  			=     retriever.retrieve(a__				,     n_docs=1  )
           self.assertTrue(out is not None  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     1
       _lowerCAmelCase						:       List[Any]  			=     self.get_dummy_custom_hf_index_retriever(from_disk=a__  )
       _lowerCAmelCase						:       str  			=     np.array(
           [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     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     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.get_dummy_custom_hf_index_retriever(from_disk=a__  )
       with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(a__  )
         _lowerCAmelCase						:       Tuple  			=     RagRetriever.from_pretrained(a__  )
         self.assertIsInstance(a__				,     a__  )
         _lowerCAmelCase						:       Tuple  			=     np.array(
             [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
         _lowerCAmelCase						:       Any  			=     retriever.retrieve(a__				,     n_docs=1  )
         self.assertTrue(out is not None  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     1
       _lowerCAmelCase						:       List[str]  			=     self.get_dummy_custom_hf_index_retriever(from_disk=a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     np.array(
           [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     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     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_dummy_custom_hf_index_retriever(from_disk=a__  )
       with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(a__  )
         _lowerCAmelCase						:       List[Any]  			=     RagRetriever.from_pretrained(a__  )
         self.assertIsInstance(a__				,     a__  )
         _lowerCAmelCase						:       List[str]  			=     np.array(
             [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
         _lowerCAmelCase						:       List[Any]  			=     retriever.retrieve(a__				,     n_docs=1  )
         self.assertTrue(out is not None  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     1
       _lowerCAmelCase						:       Tuple  			=     self.get_dummy_legacy_index_retriever()
       _lowerCAmelCase						:       Optional[Any]  			=     np.array(
           [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     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]  )				,     ["""text""", """title"""]  )
       self.assertEqual(len(doc_dicts[0]["""text"""]  )				,     a__  )
       self.assertEqual(doc_dicts[0]["""text"""][0]				,     """bar"""  )  # max inner product is reached with second doc
       self.assertEqual(doc_dicts[1]["""text"""][0]				,     """foo"""  )  # max inner product is reached with first doc
       self.assertListEqual(doc_ids.tolist()				,     [[1], [0]]  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.get_dummy_legacy_index_retriever()
       with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(a__  )
         _lowerCAmelCase						:       Tuple  			=     RagRetriever.from_pretrained(a__  )
         self.assertIsInstance(a__				,     a__  )
         _lowerCAmelCase						:       str  			=     np.array(
             [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
         _lowerCAmelCase						:       List[str]  			=     retriever.retrieve(a__				,     n_docs=1  )
         self.assertTrue(out is not None  )






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def     __A  (		self  ):
       import torch

       _lowerCAmelCase						:       Tuple  			=     1
       _lowerCAmelCase						:       int  			=     self.get_dummy_canonical_hf_index_retriever()
       _lowerCAmelCase						:       Optional[Any]  			=     [[5, 7], [10, 11]]
       _lowerCAmelCase						:       str  			=     np.array(
           [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
       _lowerCAmelCase						:       List[str]  			=     retriever(a__				,     a__				,     prefix=retriever.config.generator.prefix				,     n_docs=a__  )
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     (
           out["""context_input_ids"""],
           out["""context_attention_mask"""],
           out["""retrieved_doc_embeds"""],
       )
       self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)  )
       self.assertIsInstance(a__				,     a__  )
       self.assertIsInstance(a__				,     a__  )
       self.assertIsInstance(a__				,     np.ndarray  )

       _lowerCAmelCase						:       int  			=     retriever(
           a__				,     a__				,     prefix=retriever.config.generator.prefix				,     n_docs=a__				,     return_tensors="""pt"""				,     )
       _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     (  # noqa: F841
           out["""context_input_ids"""],
           out["""context_attention_mask"""],
           out["""retrieved_doc_embeds"""],
           out["""doc_ids"""],
       )
       self.assertEqual(retrieved_doc_embeds.shape				,     (2, n_docs, self.retrieval_vector_size)  )
       self.assertIsInstance(a__				,     torch.Tensor  )
       self.assertIsInstance(a__				,     torch.Tensor  )
       self.assertIsInstance(a__				,     torch.Tensor  )






     @require_torch
     @require_tokenizers
     @require_sentencepiece
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.get_dpr_ctx_encoder_tokenizer()
       _lowerCAmelCase						:       Optional[int]  			=     1
       _lowerCAmelCase						:       int  			=     self.get_dummy_custom_hf_index_retriever(from_disk=a__  )
       retriever.set_ctx_encoder_tokenizer(a__  )

       _lowerCAmelCase						:       Any  			=     [[5, 7], [10, 11]]
       _lowerCAmelCase						:       Any  			=     np.array(
           [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )]				,     dtype=np.floataa  )
       _lowerCAmelCase						:       List[Any]  			=     retriever(a__				,     a__				,     prefix=retriever.config.generator.prefix				,     n_docs=a__  )

       self.assertEqual(
           len(a__  )				,     6  )  # check whether the retriever output consist of 6 attributes including tokenized docs
       self.assertEqual(
           all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""")  )				,     a__  )  # check for doc token related keys in dictionary.






from typing import TYPE_CHECKING

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


__lowerCAmelCase			:     List[str]           =   {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     str           =   [
        'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XLMForMultipleChoice',
        'XLMForQuestionAnswering',
        'XLMForQuestionAnsweringSimple',
        'XLMForSequenceClassification',
        'XLMForTokenClassification',
        'XLMModel',
        'XLMPreTrainedModel',
        'XLMWithLMHeadModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCAmelCase			:     Dict           =   [
        'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFXLMForMultipleChoice',
        'TFXLMForQuestionAnsweringSimple',
        'TFXLMForSequenceClassification',
        'TFXLMForTokenClassification',
        'TFXLMMainLayer',
        'TFXLMModel',
        'TFXLMPreTrainedModel',
        'TFXLMWithLMHeadModel',
    ]


if TYPE_CHECKING:
    from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
    from .tokenization_xlm import XLMTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlm import (
            XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLMForMultipleChoice,
            XLMForQuestionAnswering,
            XLMForQuestionAnsweringSimple,
            XLMForSequenceClassification,
            XLMForTokenClassification,
            XLMModel,
            XLMPreTrainedModel,
            XLMWithLMHeadModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlm import (
            TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLMForMultipleChoice,
            TFXLMForQuestionAnsweringSimple,
            TFXLMForSequenceClassification,
            TFXLMForTokenClassification,
            TFXLMMainLayer,
            TFXLMModel,
            TFXLMPreTrainedModel,
            TFXLMWithLMHeadModel,
        )

else:
    import sys

    __lowerCAmelCase			:     Any           =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

