



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 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Optional[Any]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[Any]=13				,		lowerCAmelCase							:			int=7				,		lowerCAmelCase							:			Optional[int]=True				,		lowerCAmelCase							:			Optional[Any]=True				,		lowerCAmelCase							:			List[str]=True				,		lowerCAmelCase							:			str=True				,		lowerCAmelCase							:			Any=99				,		lowerCAmelCase							:			Dict=32				,		lowerCAmelCase							:			List[str]=2				,		lowerCAmelCase							:			str=4				,		lowerCAmelCase							:			List[str]=37				,		lowerCAmelCase							:			Dict="gelu"				,		lowerCAmelCase							:			Optional[int]=0.1				,		lowerCAmelCase							:			Any=0.1				,		lowerCAmelCase							:			List[Any]=512				,		lowerCAmelCase							:			Optional[int]=16				,		lowerCAmelCase							:			Dict=2				,		lowerCAmelCase							:			str=0.02				,		lowerCAmelCase							:			Optional[Any]=3				,		lowerCAmelCase							:			Optional[Any]=4				,		lowerCAmelCase							:			Tuple=None				,		lowerCAmelCase							:			List[Any]=0				,		)				->				int:

    """simple docstring"""







    _snake_case    :  Dict					=					parent
    _snake_case    :  int					=					batch_size
    _snake_case    :  str					=					seq_length
    _snake_case    :  List[str]					=					is_training
    _snake_case    :  Tuple					=					use_input_mask
    _snake_case    :  Optional[Any]					=					use_token_type_ids
    _snake_case    :  Any					=					use_labels
    _snake_case    :  str					=					vocab_size
    _snake_case    :  List[str]					=					hidden_size
    _snake_case    :  List[str]					=					num_hidden_layers
    _snake_case    :  Dict					=					num_attention_heads
    _snake_case    :  int					=					intermediate_size
    _snake_case    :  Any					=					hidden_act
    _snake_case    :  List[Any]					=					hidden_dropout_prob
    _snake_case    :  Tuple					=					attention_probs_dropout_prob
    _snake_case    :  List[Any]					=					max_position_embeddings
    _snake_case    :  int					=					type_vocab_size
    _snake_case    :  Dict					=					type_sequence_label_size
    _snake_case    :  List[str]					=					initializer_range
    _snake_case    :  Dict					=					num_labels
    _snake_case    :  Optional[Any]					=					num_choices
    _snake_case    :  Dict					=					scope
    _snake_case    :  List[Any]					=					projection_dim

   def      UpperCamelCase_  (					self							:			Dict)				->				List[str]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size)

    _snake_case    :  List[str]					=					None
    if self.use_input_mask:
     # follow test_modeling_tf_ctrl.py
     _snake_case    :  List[Any]					=					random_attention_mask([self.batch_size, self.seq_length])

    _snake_case    :  str					=					None
    if self.use_token_type_ids:
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size)

    _snake_case    :  Tuple					=					None
    _snake_case    :  Any					=					None
    _snake_case    :  str					=					None
    if self.use_labels:
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size]				,		self.type_sequence_label_size)
     _snake_case    :  Union[str, Any]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels)
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size]				,		self.num_choices)

    _snake_case    :  Union[str, Any]					=					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=lowerCAmelCase				,		initializer_range=self.initializer_range				,		)
    _snake_case    :  Optional[int]					=					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      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			List[str])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					TFDPRContextEncoder(config=lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  Optional[int]					=					model(lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  List[str]					=					model(lowerCAmelCase)
    self.parent.assertEqual(result.pooler_output.shape				,		(self.batch_size, self.projection_dim or self.hidden_size))

   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					TFDPRQuestionEncoder(config=lowerCAmelCase)
    _snake_case    :  List[Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  List[Any]					=					model(lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  Tuple					=					model(lowerCAmelCase)
    self.parent.assertEqual(result.pooler_output.shape				,		(self.batch_size, self.projection_dim or self.hidden_size))

   def      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			str)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					TFDPRReader(config=lowerCAmelCase)
    _snake_case    :  Any					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase)

    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      UpperCamelCase_  (					self							:			str)				->				Any:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.prepare_config_and_inputs()
    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  Union[str, Any]					=					config_and_inputs
    _snake_case    :  Union[str, Any]					=					{"""input_ids""": input_ids}
    return config, inputs_dict







@require_tf
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Any          =		(
       (
           TFDPRContextEncoder,
           TFDPRQuestionEncoder,
           TFDPRReader,
       )
       if is_tf_available()
       else ()
   )
   snake_case_					: List[str]          =		{"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {}

   snake_case_					: Tuple          =		False
   snake_case_					: str          =		False
   snake_case_					: Tuple          =		False
   snake_case_					: int          =		False
   snake_case_					: List[Any]          =		False

   def      UpperCamelCase_  (					self							:			Tuple)				->				str:

    """simple docstring"""







    _snake_case    :  List[str]					=					TFDPRModelTester(self)
    _snake_case    :  Dict					=					ConfigTester(self				,		config_class=lowerCAmelCase				,		hidden_size=37)

   def      UpperCamelCase_  (					self							:			List[Any])				->				Optional[Any]:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Any					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_context_encoder(*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_question_encoder(*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			List[str])				->				Tuple:

    """simple docstring"""







    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_reader(*lowerCAmelCase)


   @slow
   def      UpperCamelCase_  (					self							:			List[str])				->				Any:

    """simple docstring"""







    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  List[Any]					=					TFDPRContextEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  List[str]					=					TFDPRContextEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  Any					=					TFDPRQuestionEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  Union[str, Any]					=					TFDPRReader.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)







@require_tf
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   @slow
   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  List[str]					=					TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""")

    _snake_case    :  str					=					tf.constant(
        [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]])  # [CLS] hello, is my dog cute? [SEP]
    _snake_case    :  Optional[int]					=					model(lowerCAmelCase)[0]  # embedding shape = (1, 768)
    # compare the actual values for a slice.
    _snake_case    :  Union[str, Any]					=					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))





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 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			Optional[Any]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Optional[Any]=13				,		lowerCAmelCase							:			int=7				,		lowerCAmelCase							:			Optional[int]=True				,		lowerCAmelCase							:			Optional[Any]=True				,		lowerCAmelCase							:			List[str]=True				,		lowerCAmelCase							:			str=True				,		lowerCAmelCase							:			Any=99				,		lowerCAmelCase							:			Dict=32				,		lowerCAmelCase							:			List[str]=2				,		lowerCAmelCase							:			str=4				,		lowerCAmelCase							:			List[str]=37				,		lowerCAmelCase							:			Dict="gelu"				,		lowerCAmelCase							:			Optional[int]=0.1				,		lowerCAmelCase							:			Any=0.1				,		lowerCAmelCase							:			List[Any]=512				,		lowerCAmelCase							:			Optional[int]=16				,		lowerCAmelCase							:			Dict=2				,		lowerCAmelCase							:			str=0.02				,		lowerCAmelCase							:			Optional[Any]=3				,		lowerCAmelCase							:			Optional[Any]=4				,		lowerCAmelCase							:			Tuple=None				,		lowerCAmelCase							:			List[Any]=0				,		)				->				int:

    """simple docstring"""







    _snake_case    :  Dict					=					parent
    _snake_case    :  int					=					batch_size
    _snake_case    :  str					=					seq_length
    _snake_case    :  List[str]					=					is_training
    _snake_case    :  Tuple					=					use_input_mask
    _snake_case    :  Optional[Any]					=					use_token_type_ids
    _snake_case    :  Any					=					use_labels
    _snake_case    :  str					=					vocab_size
    _snake_case    :  List[str]					=					hidden_size
    _snake_case    :  List[str]					=					num_hidden_layers
    _snake_case    :  Dict					=					num_attention_heads
    _snake_case    :  int					=					intermediate_size
    _snake_case    :  Any					=					hidden_act
    _snake_case    :  List[Any]					=					hidden_dropout_prob
    _snake_case    :  Tuple					=					attention_probs_dropout_prob
    _snake_case    :  List[Any]					=					max_position_embeddings
    _snake_case    :  int					=					type_vocab_size
    _snake_case    :  Dict					=					type_sequence_label_size
    _snake_case    :  List[str]					=					initializer_range
    _snake_case    :  Dict					=					num_labels
    _snake_case    :  Optional[Any]					=					num_choices
    _snake_case    :  Dict					=					scope
    _snake_case    :  List[Any]					=					projection_dim

   def      UpperCamelCase_  (					self							:			Dict)				->				List[str]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size)

    _snake_case    :  List[str]					=					None
    if self.use_input_mask:
     # follow test_modeling_tf_ctrl.py
     _snake_case    :  List[Any]					=					random_attention_mask([self.batch_size, self.seq_length])

    _snake_case    :  str					=					None
    if self.use_token_type_ids:
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size)

    _snake_case    :  Tuple					=					None
    _snake_case    :  Any					=					None
    _snake_case    :  str					=					None
    if self.use_labels:
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size]				,		self.type_sequence_label_size)
     _snake_case    :  Union[str, Any]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels)
     _snake_case    :  Optional[Any]					=					ids_tensor([self.batch_size]				,		self.num_choices)

    _snake_case    :  Union[str, Any]					=					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=lowerCAmelCase				,		initializer_range=self.initializer_range				,		)
    _snake_case    :  Optional[int]					=					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      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			List[str])				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Dict					=					TFDPRContextEncoder(config=lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  Optional[int]					=					model(lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  List[str]					=					model(lowerCAmelCase)
    self.parent.assertEqual(result.pooler_output.shape				,		(self.batch_size, self.projection_dim or self.hidden_size))

   def      UpperCamelCase_  (					self							:			List[Any]				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Optional[int])				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					TFDPRQuestionEncoder(config=lowerCAmelCase)
    _snake_case    :  List[Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  List[Any]					=					model(lowerCAmelCase				,		token_type_ids=lowerCAmelCase)
    _snake_case    :  Tuple					=					model(lowerCAmelCase)
    self.parent.assertEqual(result.pooler_output.shape				,		(self.batch_size, self.projection_dim or self.hidden_size))

   def      UpperCamelCase_  (					self							:			List[str]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			str)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					TFDPRReader(config=lowerCAmelCase)
    _snake_case    :  Any					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase)

    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      UpperCamelCase_  (					self							:			str)				->				Any:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.prepare_config_and_inputs()
    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  Union[str, Any]					=					config_and_inputs
    _snake_case    :  Union[str, Any]					=					{"""input_ids""": input_ids}
    return config, inputs_dict







@require_tf
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Any          =		(
       (
           TFDPRContextEncoder,
           TFDPRQuestionEncoder,
           TFDPRReader,
       )
       if is_tf_available()
       else ()
   )
   snake_case_					: List[str]          =		{"""feature-extraction""": TFDPRQuestionEncoder} if is_tf_available() else {}

   snake_case_					: Tuple          =		False
   snake_case_					: str          =		False
   snake_case_					: Tuple          =		False
   snake_case_					: int          =		False
   snake_case_					: List[Any]          =		False

   def      UpperCamelCase_  (					self							:			Tuple)				->				str:

    """simple docstring"""







    _snake_case    :  List[str]					=					TFDPRModelTester(self)
    _snake_case    :  Dict					=					ConfigTester(self				,		config_class=lowerCAmelCase				,		hidden_size=37)

   def      UpperCamelCase_  (					self							:			List[Any])				->				Optional[Any]:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Any					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_context_encoder(*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_question_encoder(*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			List[str])				->				Tuple:

    """simple docstring"""







    _snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_dpr_reader(*lowerCAmelCase)


   @slow
   def      UpperCamelCase_  (					self							:			List[str])				->				Any:

    """simple docstring"""







    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  List[Any]					=					TFDPRContextEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  List[str]					=					TFDPRContextEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  Any					=					TFDPRQuestionEncoder.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)

    for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case    :  Union[str, Any]					=					TFDPRReader.from_pretrained(lowerCAmelCase)
     self.assertIsNotNone(lowerCAmelCase)







@require_tf
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   @slow
   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  List[str]					=					TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""")

    _snake_case    :  str					=					tf.constant(
        [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]])  # [CLS] hello, is my dog cute? [SEP]
    _snake_case    :  Optional[int]					=					model(lowerCAmelCase)[0]  # embedding shape = (1, 768)
    # compare the actual values for a slice.
    _snake_case    :  Union[str, Any]					=					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))
