


"""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      __snake_case							( self    :   Tuple):
      a						:		int											=      tempfile.mkdtemp()
      a						:		Union[str, Any]											=      8

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

      a						:		Optional[int]											=      os.path.join(self.tmpdirname    ,			"bart_tokenizer")
      os.makedirs(__lowercase    ,			exist_ok=__lowercase)
      a						:		Union[str, Any]											=      os.path.join(__lowercase    ,			BART_VOCAB_FILES_NAMES["vocab_file"])
      a						:		Any											=      os.path.join(__lowercase    ,			BART_VOCAB_FILES_NAMES["merges_file"])
      with open(self.vocab_file    ,			"w"    ,			encoding="utf-8") as fp:
        fp.write(json.dumps(__lowercase) + "\n")
      with open(self.merges_file    ,			"w"    ,			encoding="utf-8") as fp:
        fp.write("\n".join(__lowercase))






    def      __snake_case							( self    :   Dict):
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,			"dpr_tokenizer"))






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






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






    def      __snake_case							( self    :   Union[str, Any]):
      shutil.rmtree(self.tmpdirname)






    def      __snake_case							( self    :   Optional[Any]):
      a						:		int											=      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    :   str):
      a						:		Dict											=      self.get_dummy_dataset()
      a						:		Union[str, 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:
        a						:		Union[str, Any]											=      dataset
        a						:		str											=      RagRetriever(
            __lowercase    ,			question_encoder_tokenizer=self.get_dpr_tokenizer()    ,			generator_tokenizer=self.get_bart_tokenizer()    ,			)
      return retriever






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   bool):
      a						:		Optional[Any]											=      self.get_dummy_dataset()
      a						:		int											=      RagConfig(
          retrieval_vector_size=self.retrieval_vector_size    ,			question_encoder=DPRConfig().to_dict()    ,			generator=BartConfig().to_dict()    ,			index_name="custom"    ,			)
      if from_disk:
        a						:		List[Any]											=      os.path.join(self.tmpdirname    ,			"dataset")
        a						:		Dict											=      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						:		List[Any]											=      RagRetriever(
            __lowercase    ,			question_encoder_tokenizer=self.get_dpr_tokenizer()    ,			generator_tokenizer=self.get_bart_tokenizer()    ,			)
      else:
        a						:		Dict											=      RagRetriever(
            __lowercase    ,			question_encoder_tokenizer=self.get_dpr_tokenizer()    ,			generator_tokenizer=self.get_bart_tokenizer()    ,			index=CustomHFIndex(config.retrieval_vector_size    ,			__lowercase)    ,			)
      return retriever






    def      __snake_case							( self    :   int):
      a						:		Optional[int]											=      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						:		Dict											=      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						:		Any											=      os.path.join(self.tmpdirname    ,			"psgs_w100.tsv.pkl")
      a						:		Any											=      {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
      pickle.dump(__lowercase    ,			open(__lowercase    ,			"wb"))

      a						:		Union[str, Any]											=      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						:		Any											=      RagRetriever(
          __lowercase    ,			question_encoder_tokenizer=self.get_dpr_tokenizer()    ,			generator_tokenizer=self.get_bart_tokenizer())
      return retriever






    def      __snake_case							( self    :   Any):
      a						:		List[str]											=      1
      a						:		Optional[Any]											=      self.get_dummy_canonical_hf_index_retriever()
      a						:		Optional[Any]											=      np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
      a						:		Optional[int]											=      retriever.retrieve(__lowercase    ,			n_docs=__lowercase)
      self.assertEqual(retrieved_doc_embeds.shape    ,			(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(__lowercase)    ,			2)
      self.assertEqual(sorted(doc_dicts[0])    ,			["embeddings", "id", "text", "title"])
      self.assertEqual(len(doc_dicts[0]["id"])    ,			__lowercase)
      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    :   Optional[int]):
      a						:		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:
          a						:		Union[str, Any]											=      self.get_dummy_dataset()
          retriever.save_pretrained(__lowercase)
          a						:		int											=      RagRetriever.from_pretrained(__lowercase)
          self.assertIsInstance(__lowercase    ,			__lowercase)
          a						:		Optional[Any]											=      np.array(
              [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
          a						:		Optional[int]											=      retriever.retrieve(__lowercase    ,			n_docs=1)
          self.assertTrue(out is not None)






    def      __snake_case							( self    :   Optional[int]):
      a						:		List[Any]											=      1
      a						:		Union[str, Any]											=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowercase)
      a						:		Union[str, Any]											=      np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
      a						:		List[str]											=      retriever.retrieve(__lowercase    ,			n_docs=__lowercase)
      self.assertEqual(retrieved_doc_embeds.shape    ,			(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(__lowercase)    ,			2)
      self.assertEqual(sorted(doc_dicts[0])    ,			["embeddings", "id", "text", "title"])
      self.assertEqual(len(doc_dicts[0]["id"])    ,			__lowercase)
      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    :   Any):
      a						:		Optional[Any]											=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowercase)
      with tempfile.TemporaryDirectory() as tmp_dirname:
        retriever.save_pretrained(__lowercase)
        a						:		List[Any]											=      RagRetriever.from_pretrained(__lowercase)
        self.assertIsInstance(__lowercase    ,			__lowercase)
        a						:		int											=      np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
        a						:		str											=      retriever.retrieve(__lowercase    ,			n_docs=1)
        self.assertTrue(out is not None)






    def      __snake_case							( self    :   List[Any]):
      a						:		Optional[Any]											=      1
      a						:		Optional[Any]											=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowercase)
      a						:		Tuple											=      np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
      a						:		List[str]											=      retriever.retrieve(__lowercase    ,			n_docs=__lowercase)
      self.assertEqual(retrieved_doc_embeds.shape    ,			(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(__lowercase)    ,			2)
      self.assertEqual(sorted(doc_dicts[0])    ,			["embeddings", "id", "text", "title"])
      self.assertEqual(len(doc_dicts[0]["id"])    ,			__lowercase)
      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    :   Any):
      a						:		Dict											=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowercase)
      with tempfile.TemporaryDirectory() as tmp_dirname:
        retriever.save_pretrained(__lowercase)
        a						:		Any											=      RagRetriever.from_pretrained(__lowercase)
        self.assertIsInstance(__lowercase    ,			__lowercase)
        a						:		Dict											=      np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
        a						:		List[Any]											=      retriever.retrieve(__lowercase    ,			n_docs=1)
        self.assertTrue(out is not None)






    def      __snake_case							( self    :   Any):
      a						:		Tuple											=      1
      a						:		Dict											=      self.get_dummy_legacy_index_retriever()
      a						:		Union[str, Any]											=      np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
      a						:		int											=      retriever.retrieve(__lowercase    ,			n_docs=__lowercase)
      self.assertEqual(retrieved_doc_embeds.shape    ,			(2, n_docs, self.retrieval_vector_size))
      self.assertEqual(len(__lowercase)    ,			2)
      self.assertEqual(sorted(doc_dicts[0])    ,			["text", "title"])
      self.assertEqual(len(doc_dicts[0]["text"])    ,			__lowercase)
      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      __snake_case							( self    :   Tuple):
      a						:		Optional[Any]											=      self.get_dummy_legacy_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
        retriever.save_pretrained(__lowercase)
        a						:		Tuple											=      RagRetriever.from_pretrained(__lowercase)
        self.assertIsInstance(__lowercase    ,			__lowercase)
        a						:		Any											=      np.array(
            [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
        a						:		str											=      retriever.retrieve(__lowercase    ,			n_docs=1)
        self.assertTrue(out is not None)






    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      __snake_case							( self    :   str):
      import torch

      a						:		Any											=      1
      a						:		Any											=      self.get_dummy_canonical_hf_index_retriever()
      a						:		List[Any]											=      [[5, 7], [10, 11]]
      a						:		Union[str, Any]											=      np.array(
          [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]    ,			dtype=np.floataa)
      a						:		Tuple											=      retriever(__lowercase    ,			__lowercase    ,			prefix=retriever.config.generator.prefix    ,			n_docs=__lowercase)
      a						:		str											=      (
          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(__lowercase    ,			__lowercase)
      self.assertIsInstance(__lowercase    ,			__lowercase)
      self.assertIsInstance(__lowercase    ,			np.ndarray)

      a						:		Union[str, Any]											=      retriever(
          __lowercase    ,			__lowercase    ,			prefix=retriever.config.generator.prefix    ,			n_docs=__lowercase    ,			return_tensors="pt"    ,			)
      a						:		Dict											=      (  # 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(__lowercase    ,			torch.Tensor)
      self.assertIsInstance(__lowercase    ,			torch.Tensor)
      self.assertIsInstance(__lowercase    ,			torch.Tensor)






    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def      __snake_case							( self    :   List[Any]):
      a						:		List[Any]											=      self.get_dpr_ctx_encoder_tokenizer()
      a						:		List[Any]											=      1
      a						:		Any											=      self.get_dummy_custom_hf_index_retriever(from_disk=__lowercase)
      retriever.set_ctx_encoder_tokenizer(__lowercase)

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

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










"""simple docstring"""







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






    @property
    def      __snake_case							( self    :   int):
      if self.task == "multiple-choice":
        a						:		List[Any]											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		int											=      {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("global_attention_mask", dynamic_axis),
          ])






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






