

def 					__lowercase      (     _A			)     ->						float:
      if not nums:  # Makes sure that the list is not empty
            raise ValueError("""List is empty"""			)

      SCREAMING_SNAKE_CASE :      Any       =   sum(_A			) / len(_A			)  # Calculate the average
      return sum(abs(x - average			) for x in nums			) / len(_A			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()







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__       (   UpperCAmelCase       ):







  """simple docstring"""




  def  _lowercase      (			self    : Optional[int]		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   tempfile.mkdtemp()
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   8

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

        SCREAMING_SNAKE_CASE :      int       =   os.path.join(self.tmpdirname				,				"""bart_tokenizer"""		)
        os.makedirs(UpperCAmelCase__				,				exist_ok=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   os.path.join(UpperCAmelCase__				,				BART_VOCAB_FILES_NAMES["""vocab_file"""]		)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   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  _lowercase      (			self    : Optional[Any]		)					->DPRQuestionEncoderTokenizer:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[Any]		)					->DPRContextEncoderTokenizer:
        """simple docstring"""







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


  def  _lowercase      (			self    : Optional[int]		)					->BartTokenizer:
        """simple docstring"""







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


  def  _lowercase      (			self    : Any		)					->List[str]:
        """simple docstring"""







        shutil.rmtree(self.tmpdirname		)


  def  _lowercase      (			self    : Tuple		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   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  _lowercase      (			self    : Union[str, Any]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.get_dummy_dataset()
        SCREAMING_SNAKE_CASE :      str       =   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 :      Tuple       =   dataset
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   RagRetriever(
                  UpperCAmelCase__				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				)
        return retriever


  def  _lowercase      (			self    : List[Any]				,				UpperCAmelCase__    : bool		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   self.get_dummy_dataset()
        SCREAMING_SNAKE_CASE :      Dict       =   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 :      List[str]       =   os.path.join(self.tmpdirname				,				"""dataset"""		)
              SCREAMING_SNAKE_CASE :      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
              SCREAMING_SNAKE_CASE :      Any       =   RagRetriever(
                  UpperCAmelCase__				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				)
        else:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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  _lowercase      (			self    : int		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      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		)

        SCREAMING_SNAKE_CASE :      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"""		)		)

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

        SCREAMING_SNAKE_CASE :      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				,				)
        SCREAMING_SNAKE_CASE :      Any       =   RagRetriever(
            UpperCAmelCase__				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()		)
        return retriever


  def  _lowercase      (			self    : Dict		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   1
        SCREAMING_SNAKE_CASE :      str       =   self.get_dummy_canonical_hf_index_retriever()
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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  _lowercase      (			self    : List[str]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      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:
                    SCREAMING_SNAKE_CASE :      Optional[int]       =   self.get_dummy_dataset()
                    retriever.save_pretrained(UpperCAmelCase__		)
                    SCREAMING_SNAKE_CASE :      List[Any]       =   RagRetriever.from_pretrained(UpperCAmelCase__		)
                    self.assertIsInstance(UpperCAmelCase__				,				UpperCAmelCase__		)
                    SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.array(
                        [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
                    SCREAMING_SNAKE_CASE :      Dict       =   retriever.retrieve(UpperCAmelCase__				,				n_docs=1		)
                    self.assertTrue(out is not None		)


  def  _lowercase      (			self    : List[Any]		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   1
        SCREAMING_SNAKE_CASE :      str       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[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  _lowercase      (			self    : Optional[int]		)					->Optional[Any]:
        """simple docstring"""







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


  def  _lowercase      (			self    : Dict		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   1
        SCREAMING_SNAKE_CASE :      List[str]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   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  _lowercase      (			self    : Union[str, Any]		)					->Any:
        """simple docstring"""







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


  def  _lowercase      (			self    : Tuple		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   1
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.get_dummy_legacy_index_retriever()
        SCREAMING_SNAKE_CASE :      int       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   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  _lowercase      (			self    : Any		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_dummy_legacy_index_retriever()
        with tempfile.TemporaryDirectory() as tmp_dirname:
              retriever.save_pretrained(UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   RagRetriever.from_pretrained(UpperCAmelCase__		)
              self.assertIsInstance(UpperCAmelCase__				,				UpperCAmelCase__		)
              SCREAMING_SNAKE_CASE :      str       =   np.array(
                  [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
              SCREAMING_SNAKE_CASE :      Dict       =   retriever.retrieve(UpperCAmelCase__				,				n_docs=1		)
              self.assertTrue(out is not None		)


  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def  _lowercase      (			self    : Tuple		)					->Tuple:
        """simple docstring"""







        import torch

        SCREAMING_SNAKE_CASE :      int       =   1
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.get_dummy_canonical_hf_index_retriever()
        SCREAMING_SNAKE_CASE :      str       =   [[5, 7], [1_0, 1_1]]
        SCREAMING_SNAKE_CASE :      Tuple       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE :      int       =   retriever(UpperCAmelCase__				,				UpperCAmelCase__				,				prefix=retriever.config.generator.prefix				,				n_docs=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   (
            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		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   retriever(
            UpperCAmelCase__				,				UpperCAmelCase__				,				prefix=retriever.config.generator.prefix				,				n_docs=UpperCAmelCase__				,				return_tensors="""pt"""				,				)
        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      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(UpperCAmelCase__				,				torch.Tensor		)
        self.assertIsInstance(UpperCAmelCase__				,				torch.Tensor		)
        self.assertIsInstance(UpperCAmelCase__				,				torch.Tensor		)




  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def  _lowercase      (			self    : Union[str, Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.get_dpr_ctx_encoder_tokenizer()
        SCREAMING_SNAKE_CASE :      Optional[int]       =   1
        SCREAMING_SNAKE_CASE :      List[Any]       =   self.get_dummy_custom_hf_index_retriever(from_disk=UpperCAmelCase__		)
        retriever.set_ctx_encoder_tokenizer(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[str]       =   [[5, 7], [1_0, 1_1]]
        SCREAMING_SNAKE_CASE :      Optional[int]       =   np.array(
            [np.ones(self.retrieval_vector_size		), -np.ones(self.retrieval_vector_size		)]				,				dtype=np.floataa		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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.




