

"""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						(       _lowerCamelCase				):



    """simple docstring"""



    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  tempfile.mkdtemp()
         A__										=  8

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

         A__										=  os.path.join(self.tmpdirname    ,						"""bart_tokenizer"""       )
         os.makedirs(UpperCamelCase    ,						exist_ok=UpperCamelCase       )
         A__										=  os.path.join(UpperCamelCase    ,						BART_VOCAB_FILES_NAMES["""vocab_file"""]       )
         A__										=  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: Optional[Any]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: Optional[int]       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


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

    def 				UpperCamelCase	(     self: str       ):






         """simple docstring"""


         shutil.rmtree(self.tmpdirname       )

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  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]       ):






         """simple docstring"""


         A__										=  self.get_dummy_dataset()
         A__										=  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:
              A__										=  dataset
              A__										=  RagRetriever(
                  UpperCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()    ,						)
         return retriever

    def 				UpperCamelCase	(     self: Dict    ,						UpperCamelCase: bool       ):






         """simple docstring"""


         A__										=  self.get_dummy_dataset()
         A__										=  RagConfig(
             retrieval_vector_size=self.retrieval_vector_size    ,						question_encoder=DPRConfig().to_dict()    ,						generator=BartConfig().to_dict()    ,						index_name="""custom"""    ,						)
         if from_disk:
              A__										=  os.path.join(self.tmpdirname    ,						"""dataset"""       )
              A__										=  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
              A__										=  RagRetriever(
                  UpperCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()    ,						)
         else:
              A__										=  RagRetriever(
                  UpperCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()    ,						index=CustomHFIndex(config.retrieval_vector_size    ,						UpperCamelCase       )    ,						)
         return retriever

    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  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       )

         A__										=  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"""       )       )

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

         A__										=  RagConfig(
             retrieval_vector_size=self.retrieval_vector_size    ,						question_encoder=DPRConfig().to_dict()    ,						generator=BartConfig().to_dict()    ,						index_name="""legacy"""    ,						index_path=self.tmpdirname    ,						)
         A__										=  RagRetriever(
             UpperCamelCase    ,						question_encoder_tokenizer=self.get_dpr_tokenizer()    ,						generator_tokenizer=self.get_bart_tokenizer()       )
         return retriever

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  1
         A__										=  self.get_dummy_canonical_hf_index_retriever()
         A__										=  np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
         A__			,				A__			,				A__										=  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]       ):






         """simple docstring"""


         A__										=  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:
                   A__										=  self.get_dummy_dataset()
                   retriever.save_pretrained(UpperCamelCase       )
                   A__										=  RagRetriever.from_pretrained(UpperCamelCase       )
                   self.assertIsInstance(UpperCamelCase    ,						UpperCamelCase       )
                   A__										=  np.array(
                       [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
                   A__										=  retriever.retrieve(UpperCamelCase    ,						n_docs=1       )
                   self.assertTrue(out is not None       )

    def 				UpperCamelCase	(     self: Union[str, Any]       ):






         """simple docstring"""


         A__										=  1
         A__										=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase       )
         A__										=  np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
         A__			,				A__			,				A__										=  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: Any       ):






         """simple docstring"""


         A__										=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase       )
         with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(UpperCamelCase       )
              A__										=  RagRetriever.from_pretrained(UpperCamelCase       )
              self.assertIsInstance(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  np.array(
                  [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
              A__										=  retriever.retrieve(UpperCamelCase    ,						n_docs=1       )
              self.assertTrue(out is not None       )

    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  1
         A__										=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase       )
         A__										=  np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
         A__			,				A__			,				A__										=  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: Union[str, Any]       ):






         """simple docstring"""


         A__										=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase       )
         with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(UpperCamelCase       )
              A__										=  RagRetriever.from_pretrained(UpperCamelCase       )
              self.assertIsInstance(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  np.array(
                  [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
              A__										=  retriever.retrieve(UpperCamelCase    ,						n_docs=1       )
              self.assertTrue(out is not None       )

    def 				UpperCamelCase	(     self: Any       ):






         """simple docstring"""


         A__										=  1
         A__										=  self.get_dummy_legacy_index_retriever()
         A__										=  np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
         A__			,				A__			,				A__										=  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]       )    ,						["""text""", """title"""]       )
         self.assertEqual(len(doc_dicts[0]["""text"""]       )    ,						UpperCamelCase       )
         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 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         A__										=  self.get_dummy_legacy_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(UpperCamelCase       )
              A__										=  RagRetriever.from_pretrained(UpperCamelCase       )
              self.assertIsInstance(UpperCamelCase    ,						UpperCamelCase       )
              A__										=  np.array(
                  [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
              A__										=  retriever.retrieve(UpperCamelCase    ,						n_docs=1       )
              self.assertTrue(out is not None       )

    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 				UpperCamelCase	(     self: Tuple       ):






         """simple docstring"""


         import torch

         A__										=  1
         A__										=  self.get_dummy_canonical_hf_index_retriever()
         A__										=  [[5, 7], [10, 11]]
         A__										=  np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
         A__										=  retriever(UpperCamelCase    ,						UpperCamelCase    ,						prefix=retriever.config.generator.prefix    ,						n_docs=UpperCamelCase       )
         A__			,				A__			,				A__										=  (
             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(UpperCamelCase    ,						UpperCamelCase       )
         self.assertIsInstance(UpperCamelCase    ,						UpperCamelCase       )
         self.assertIsInstance(UpperCamelCase    ,						np.ndarray       )

         A__										=  retriever(
             UpperCamelCase    ,						UpperCamelCase    ,						prefix=retriever.config.generator.prefix    ,						n_docs=UpperCamelCase    ,						return_tensors="""pt"""    ,						)
         A__			,				A__			,				A__			,				A__										=  (  # 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(UpperCamelCase    ,						torch.Tensor       )
         self.assertIsInstance(UpperCamelCase    ,						torch.Tensor       )
         self.assertIsInstance(UpperCamelCase    ,						torch.Tensor       )





    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 				UpperCamelCase	(     self: Dict       ):






         """simple docstring"""


         A__										=  self.get_dpr_ctx_encoder_tokenizer()
         A__										=  1
         A__										=  self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase       )
         retriever.set_ctx_encoder_tokenizer(UpperCamelCase       )

         A__										=  [[5, 7], [10, 11]]
         A__										=  np.array(
             [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]    ,						dtype=np.floataa       )
         A__										=  retriever(UpperCamelCase    ,						UpperCamelCase    ,						prefix=retriever.config.generator.prefix    ,						n_docs=UpperCamelCase       )

         self.assertEqual(
             len(UpperCamelCase       )    ,						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""")       )    ,						UpperCamelCase       )  # check for doc token related keys in dictionary.









"""simple docstring"""



SCREAMING_SNAKE_CASE_					:	str							  =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich






