




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


UpperCamelCase								=					logging.get_logger(__name__)

UpperCamelCase								=					{"vocab_file": "spiece.model"}

UpperCamelCase								=					{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}





class 			__lowerCamelCase	(   _SCREAMING_SNAKE_CASE   ):




				"""simple docstring"""

				def __init__(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=False       ,     SCREAMING_SNAKE_CASE__					:					List[str]=True       ,     SCREAMING_SNAKE_CASE__					:					Dict=False       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]="<s>"       ,     SCREAMING_SNAKE_CASE__					:					List[str]="</s>"       ,     SCREAMING_SNAKE_CASE__					:					str="<unk>"       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="<sep>"       ,     SCREAMING_SNAKE_CASE__					:					Dict="<pad>"       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]="<cls>"       ,     SCREAMING_SNAKE_CASE__					:					str="<mask>"       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=["<eop>", "<eod>"]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Dict[str, Any]] = None       ,     **SCREAMING_SNAKE_CASE__					:					int       ,     )				->	None:
									lowerCAmelCase__          =			AddedToken(SCREAMING_SNAKE_CASE__       ,     lstrip=SCREAMING_SNAKE_CASE__       ,     rstrip=SCREAMING_SNAKE_CASE__       ) if isinstance(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ) else mask_token

									lowerCAmelCase__          =			{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    do_lower_case=SCREAMING_SNAKE_CASE__       ,     remove_space=SCREAMING_SNAKE_CASE__       ,     keep_accents=SCREAMING_SNAKE_CASE__       ,     bos_token=SCREAMING_SNAKE_CASE__       ,     eos_token=SCREAMING_SNAKE_CASE__       ,     unk_token=SCREAMING_SNAKE_CASE__       ,     sep_token=SCREAMING_SNAKE_CASE__       ,     pad_token=SCREAMING_SNAKE_CASE__       ,     cls_token=SCREAMING_SNAKE_CASE__       ,     mask_token=SCREAMING_SNAKE_CASE__       ,     additional_special_tokens=SCREAMING_SNAKE_CASE__       ,     sp_model_kwargs=self.sp_model_kwargs       ,     **SCREAMING_SNAKE_CASE__       ,     )

									lowerCAmelCase__          =			3

									lowerCAmelCase__          =			do_lower_case
									lowerCAmelCase__          =			remove_space
									lowerCAmelCase__          =			keep_accents
									lowerCAmelCase__          =			vocab_file

									lowerCAmelCase__          =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
									self.sp_model.Load(SCREAMING_SNAKE_CASE__       )

									try:
														import jieba
									except ModuleNotFoundError as error:
														raise error.__class__(
														    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
														    "See https://pypi.org/project/jieba/ for installation."       )
									lowerCAmelCase__          =			jieba
									lowerCAmelCase__          =			str.maketrans(" \n"       ,     "\u2582\u2583"       )


				@property
				# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
				def        a				(	self					:					List[str]       )				->	int:
									return len(self.sp_model       )


				def        a				(	self					:					Optional[Any]       )				->	List[str]:
									lowerCAmelCase__          =			{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__       ): i for i in range(self.vocab_size       )}
									vocab.update(self.added_tokens_encoder       )
									return vocab


				def __getstate__(	self					:					Dict       )				->	Optional[Any]:
									lowerCAmelCase__          =			self.__dict__.copy()
									lowerCAmelCase__          =			None
									return state


				def __setstate__(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	int:
									lowerCAmelCase__          =			d

									# for backward compatibility
									if not hasattr(self       ,     "sp_model_kwargs"       ):
														lowerCAmelCase__          =			{}

									lowerCAmelCase__          =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
									self.sp_model.Load(self.vocab_file       )


				def        a				(	self					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Optional[Any]:
									if self.remove_space:
														lowerCAmelCase__          =			""" """.join(inputs.strip().split()       )
									else:
														lowerCAmelCase__          =			inputs
									lowerCAmelCase__          =			outputs.replace("``"       ,     "\""       ).replace("''"       ,     "\""       )

									if not self.keep_accents:
														lowerCAmelCase__          =			unicodedata.normalize("NFKD"       ,     SCREAMING_SNAKE_CASE__       )
														lowerCAmelCase__          =			"""""".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE__       )]       )
									if self.do_lower_case:
														lowerCAmelCase__          =			outputs.lower()

									return outputs


				def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	List[str]:
									lowerCAmelCase__          =			self.preprocess_text(SCREAMING_SNAKE_CASE__       )
									lowerCAmelCase__          =			self.sp_model.encode(SCREAMING_SNAKE_CASE__       ,     out_type=SCREAMING_SNAKE_CASE__       )
									lowerCAmelCase__          =			[]
									for piece in pieces:
														if len(SCREAMING_SNAKE_CASE__       ) > 1 and piece[-1] == str(","       ) and piece[-2].isdigit():
																			lowerCAmelCase__          =			self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE__       ,     ""       )       )
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																								if len(cur_pieces[0]       ) == 1:
																													lowerCAmelCase__          =			cur_pieces[1:]
																								else:
																													lowerCAmelCase__          =			cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]       )
																			new_pieces.extend(SCREAMING_SNAKE_CASE__       )
														else:
																			new_pieces.append(SCREAMING_SNAKE_CASE__       )

									return new_pieces


				def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Optional[int]:
									return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__       )


				def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[int]:
									return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__       )


				def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	int:
									lowerCAmelCase__          =			"""""".join(SCREAMING_SNAKE_CASE__       ).replace(SCREAMING_SNAKE_CASE__       ,     " "       ).strip()
									return out_string


				def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[List[int]] = None       )				->	List[int]:
									lowerCAmelCase__          =			[self.sep_token_id]
									lowerCAmelCase__          =			[self.cls_token_id]
									if token_ids_a is None:
														return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls


				def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[List[int]] = None       ,     SCREAMING_SNAKE_CASE__					:					bool = False       )				->	List[int]:

									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=SCREAMING_SNAKE_CASE__       ,     token_ids_a=SCREAMING_SNAKE_CASE__       ,     already_has_special_tokens=SCREAMING_SNAKE_CASE__       )

									if token_ids_a is not None:
														return ([0] * len(SCREAMING_SNAKE_CASE__       )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__       )) + [1, 1]
									return ([0] * len(SCREAMING_SNAKE_CASE__       )) + [1, 1]


				def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[int]       ,     SCREAMING_SNAKE_CASE__					:					Optional[List[int]] = None       )				->	List[int]:
									lowerCAmelCase__          =			[self.sep_token_id]
									lowerCAmelCase__          =			[2]

									if token_ids_a is None:
														return len(token_ids_a + sep       ) * [0] + cls_segment_id
									return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id


				def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[str] = None       )				->	Tuple[str]:
									if not os.path.isdir(SCREAMING_SNAKE_CASE__       ):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'       )
														return
									lowerCAmelCase__          =			os.path.join(
									    SCREAMING_SNAKE_CASE__       ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

									if os.path.abspath(self.vocab_file       ) != os.path.abspath(SCREAMING_SNAKE_CASE__       ) and os.path.isfile(self.vocab_file       ):
														copyfile(self.vocab_file       ,     SCREAMING_SNAKE_CASE__       )
									elif not os.path.isfile(self.vocab_file       ):
														with open(SCREAMING_SNAKE_CASE__       ,     "wb"       ) as fi:
																			lowerCAmelCase__          =			self.sp_model.serialized_model_proto()
																			fi.write(SCREAMING_SNAKE_CASE__       )

									return (out_vocab_file,)


				def        a				(	self					:					Optional[Any]       ,     *SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     **SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	List[Any]:
									lowerCAmelCase__          =			super()._decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
									lowerCAmelCase__          =			text.replace(" "       ,     ""       ).replace("\u2582"       ,     " "       ).replace("\u2583"       ,     "\n"       )
									return text






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import numpy
     import tensorflow as tf

     from transformers import (
         TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
         BertConfig,
         DPRConfig,
         TFDPRContextEncoder,
         TFDPRQuestionEncoder,
         TFDPRReader,
     )





class 			__lowerCamelCase	:




    """simple docstring"""

    def __init__(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=13       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=7       ,     SCREAMING_SNAKE_CASE__					:					str=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					Dict=True       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					int=99       ,     SCREAMING_SNAKE_CASE__					:					List[str]=32       ,     SCREAMING_SNAKE_CASE__					:					Tuple=2       ,     SCREAMING_SNAKE_CASE__					:					List[str]=4       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=37       ,     SCREAMING_SNAKE_CASE__					:					List[Any]="gelu"       ,     SCREAMING_SNAKE_CASE__					:					int=0.1       ,     SCREAMING_SNAKE_CASE__					:					Dict=0.1       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=512       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=16       ,     SCREAMING_SNAKE_CASE__					:					List[str]=2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=0.02       ,     SCREAMING_SNAKE_CASE__					:					Tuple=3       ,     SCREAMING_SNAKE_CASE__					:					int=4       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=None       ,     SCREAMING_SNAKE_CASE__					:					str=0       ,     )				->	List[Any]:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			seq_length
         lowerCAmelCase__          =			is_training
         lowerCAmelCase__          =			use_input_mask
         lowerCAmelCase__          =			use_token_type_ids
         lowerCAmelCase__          =			use_labels
         lowerCAmelCase__          =			vocab_size
         lowerCAmelCase__          =			hidden_size
         lowerCAmelCase__          =			num_hidden_layers
         lowerCAmelCase__          =			num_attention_heads
         lowerCAmelCase__          =			intermediate_size
         lowerCAmelCase__          =			hidden_act
         lowerCAmelCase__          =			hidden_dropout_prob
         lowerCAmelCase__          =			attention_probs_dropout_prob
         lowerCAmelCase__          =			max_position_embeddings
         lowerCAmelCase__          =			type_vocab_size
         lowerCAmelCase__          =			type_sequence_label_size
         lowerCAmelCase__          =			initializer_range
         lowerCAmelCase__          =			num_labels
         lowerCAmelCase__          =			num_choices
         lowerCAmelCase__          =			scope
         lowerCAmelCase__          =			projection_dim


    def        a				(	self					:					Any       )				->	Any:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			None
         if self.use_input_mask:
              # follow test_modeling_tf_ctrl.py
              lowerCAmelCase__          =			random_attention_mask([self.batch_size, self.seq_length]       )

         lowerCAmelCase__          =			None
         if self.use_token_type_ids:
              lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.type_vocab_size       )

         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			None
         lowerCAmelCase__          =			None
         if self.use_labels:
              lowerCAmelCase__          =			ids_tensor([self.batch_size]       ,     self.type_sequence_label_size       )
              lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.num_labels       )
              lowerCAmelCase__          =			ids_tensor([self.batch_size]       ,     self.num_choices       )

         lowerCAmelCase__          =			BertConfig(
             vocab_size=self.vocab_size       ,     hidden_size=self.hidden_size       ,     num_hidden_layers=self.num_hidden_layers       ,     num_attention_heads=self.num_attention_heads       ,     intermediate_size=self.intermediate_size       ,     hidden_act=self.hidden_act       ,     hidden_dropout_prob=self.hidden_dropout_prob       ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     type_vocab_size=self.type_vocab_size       ,     is_decoder=SCREAMING_SNAKE_CASE__       ,     initializer_range=self.initializer_range       ,     )
         lowerCAmelCase__          =			DPRConfig(projection_dim=self.projection_dim       ,     **config.to_dict()       )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRContextEncoder(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRQuestionEncoder(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     token_type_ids=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.parent.assertEqual(result.pooler_output.shape       ,     (self.batch_size, self.projection_dim or self.hidden_size)       )


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	List[Any]:
         lowerCAmelCase__          =			TFDPRReader(config=SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       )

         self.parent.assertEqual(result.start_logits.shape       ,     (self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape       ,     (self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.relevance_logits.shape       ,     (self.batch_size,)       )


    def        a				(	self					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			self.prepare_config_and_inputs()
         (
             (
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		(
             lowerCAmelCase__
         )     ,		
         )          =			config_and_inputs
         lowerCAmelCase__          =			{"input_ids": input_ids}
         return config, inputs_dict






@require_tf
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




    """simple docstring"""

    snake_case__ =				(
        (
            TFDPRContextEncoder,
            TFDPRQuestionEncoder,
            TFDPRReader,
        )
        if is_tf_available()
        else ()
    )
    snake_case__ =				{"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					Dict       )				->	int:
         lowerCAmelCase__          =			TFDPRModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     hidden_size=37       )


    def        a				(	self					:					List[Any]       )				->	List[Any]:
         self.config_tester.run_common_tests()


    def        a				(	self					:					Tuple       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_context_encoder(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Union[str, Any]       )				->	Optional[int]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_question_encoder(*SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					List[Any]       )				->	Dict:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_dpr_reader(*SCREAMING_SNAKE_CASE__       )


    @slow
    def        a				(	self					:					List[str]       )				->	Union[str, Any]:
         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRContextEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )

         for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__          =			TFDPRReader.from_pretrained(SCREAMING_SNAKE_CASE__       )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__       )






@require_tf
class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    @slow
    def        a				(	self					:					Optional[Any]       )				->	List[str]:
         lowerCAmelCase__          =			TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"       )

         lowerCAmelCase__          =			tf.constant(
             [[101, 7_592, 1_010, 2_003, 2_026, 3_899, 10_140, 1_029, 102]]       )  # [CLS] hello, is my dog cute? [SEP]
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )[0]  # embedding shape = (1, 768)
         # compare the actual values for a slice.
         lowerCAmelCase__          =			tf.constant(
             [
                 [
                     0.03_236_253,
                     0.12_753_335,
                     0.16_818_509,
                     0.00_279_786,
                     0.3_896_933,
                     0.24_264_945,
                     0.2_178_971,
                     -0.02_335_227,
                     -0.08_481_959,
                     -0.14_324_117,
                 ]
             ]       )
         self.assertTrue(numpy.allclose(output[:, :10].numpy()       ,     expected_slice.numpy()       ,     atol=1e-4       )       )
