


"""simple docstring"""







import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
 from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
 __lowercase    					=      get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
 from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

__lowercase    					=      128022
__lowercase    					=      128028






@require_sentencepiece
class 						_A  (  _a    ,unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							List[Any]	=       MaMaaaTokenizer
    UpperCAmelCase       :							List[str]	=       False
    UpperCAmelCase       :							Tuple	=       False
    UpperCAmelCase       :							str	=       True






    def 	__snake_case							(					self				:    int):
      super().setUp()

      a						:		Any      							=			["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
      a						:		Any      							=			dict(zip(__UpperCAmelCase				, range(len(__UpperCAmelCase))))
      a						:		List[str]      							=			Path(self.tmpdirname)
      save_json(__UpperCAmelCase				, save_dir / VOCAB_FILES_NAMES["vocab_file"])
      if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
        copyfile(__UpperCAmelCase				, save_dir / VOCAB_FILES_NAMES["spm_file"])

      a						:		Tuple      							=			MaMaaaTokenizer.from_pretrained(self.tmpdirname)
      tokenizer.save_pretrained(self.tmpdirname)






    def 	__snake_case							(					self				:    str				, **__UpperCAmelCase				:    str):
      return MaMaaaTokenizer.from_pretrained(self.tmpdirname				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    List[Any]):
      return (
          "This is a test",
          "This is a test",
      )






    def 	__snake_case							(					self				:    Tuple):
      a						:		int      							=			"</s>"
      a						:		Dict      							=			0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase)				, __UpperCAmelCase)
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase)				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		Tuple      							=			self.get_tokenizer()
      a						:		Optional[Any]      							=			list(tokenizer.get_vocab().keys())

      self.assertEqual(vocab_keys[0]				, "</s>")
      self.assertEqual(vocab_keys[1]				, "<unk>")
      self.assertEqual(vocab_keys[-1]				, "<s>")
      self.assertEqual(len(__UpperCAmelCase)				, tokenizer.vocab_size + len(tokenizer.get_added_vocab()))






    @unittest.skip("Skip this test while all models are still to be uploaded.")
    def 	__snake_case							(					self				:    str):
      pass






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Tuple      							=			self.get_tokenizer()

      a						:		Dict      							=			tokenizer.tokenize("This is a test")
      self.assertListEqual(__UpperCAmelCase				, ["▁This", "▁is", "▁a", "▁t", "est"])

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__UpperCAmelCase)				, [2, 3, 4, 5, 6]				, )

      a						:		str      							=			tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6])
      self.assertListEqual(__UpperCAmelCase				, ["▁This", "▁is", "▁a", "▁t", "est"])

      a						:		Optional[Any]      							=			tokenizer.convert_tokens_to_string(__UpperCAmelCase)
      self.assertEqual(__UpperCAmelCase				, "This is a test")






    @slow
    def 	__snake_case							(					self				:    Tuple):
      # fmt: off
      a						:		Optional[int]      							=			{"input_ids": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
      # fmt: on

      self.tokenizer_integration_test_util(
          expected_encoding=__UpperCAmelCase				, model_name="facebook/m2m100_418M"				, revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"				, )






@require_torch
@require_sentencepiece
@require_tokenizers
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Union[str, Any]	=       """facebook/m2m100_418M"""
    UpperCAmelCase       :							Union[str, Any]	=       [
        """In my opinion, there are two levels of response from the French government.""",
        """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
    ]
    UpperCAmelCase       :							Dict	=       [
        """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
        """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
    ]

    # fmt: off
    UpperCAmelCase       :							List[str]	=       [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]






    @classmethod
    def 	__snake_case							(					cls				:    List[str]):
      a						:		MaMaaaTokenizer      							=			MaMaaaTokenizer.from_pretrained(
          cls.checkpoint_name				, src_lang="en"				, tgt_lang="fr")
      a						:		List[str]      							=			1
      return cls






    def 	__snake_case							(					self				:    Union[str, Any]):
      self.assertEqual(self.tokenizer.get_lang_id("ar")				, 128006)
      self.assertEqual(self.tokenizer.get_lang_id("en")				, 128022)
      self.assertEqual(self.tokenizer.get_lang_id("ro")				, 128076)
      self.assertEqual(self.tokenizer.get_lang_id("mr")				, 128063)






    def 	__snake_case							(					self				:    Tuple):
      a						:		Union[str, Any]      							=			self.tokenizer.get_vocab()
      self.assertEqual(len(__UpperCAmelCase)				, self.tokenizer.vocab_size)
      self.assertEqual(vocab["<unk>"]				, 3)
      self.assertIn(self.tokenizer.get_lang_token("en")				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[Any]):
      a						:		int      							=			"en"
      a						:		int      							=			self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
      self.assertListEqual(self.expected_src_tokens				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    Any):
      self.assertIn(__UpperCAmelCase				, self.tokenizer.all_special_ids)
      # fmt: off
      a						:		List[Any]      							=			[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
      # fmt: on
      a						:		str      							=			self.tokenizer.decode(__UpperCAmelCase				, skip_special_tokens=__UpperCAmelCase)
      a						:		Optional[Any]      							=			self.tokenizer.decode(generated_ids[1:]				, skip_special_tokens=__UpperCAmelCase)
      self.assertEqual(__UpperCAmelCase				, __UpperCAmelCase)
      self.assertNotIn(self.tokenizer.eos_token				, __UpperCAmelCase)






    def 	__snake_case							(					self				:    List[str]):
      a						:		Optional[int]      							=			tempfile.mkdtemp()
      a						:		Union[str, Any]      							=			self.tokenizer.lang_token_to_id
      self.tokenizer.save_pretrained(__UpperCAmelCase)
      a						:		int      							=			MaMaaaTokenizer.from_pretrained(__UpperCAmelCase)
      self.assertDictEqual(new_tok.lang_token_to_id				, __UpperCAmelCase)






    @require_torch
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Dict      							=			"en"
      a						:		List[str]      							=			"fr"

      a						:		str      							=			self.tokenizer(self.src_text				, text_target=self.tgt_text				, padding=__UpperCAmelCase				, return_tensors="pt")

      a						:		Any      							=			shift_tokens_right(
          batch["labels"]				, self.tokenizer.pad_token_id				, self.tokenizer.eos_token_id)

      for k in batch:
        a						:		str      							=			batch[k].tolist()
      # batch = {k: v.tolist() for k,v in batch.items()}
      # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
      # batch.decoder_inputs_ids[0][0] ==
      assert batch.input_ids[1][0] == EN_CODE
      assert batch.input_ids[1][-1] == 2
      assert batch.labels[1][0] == FR_CODE
      assert batch.labels[1][-1] == 2
      assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]






    @require_torch
    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Dict      							=			"mr"
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("mr")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])

      a						:		List[Any]      							=			"zh"
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("zh")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])






    @require_torch
    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Any      							=			"mr"
      self.tokenizer._switch_to_target_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("mr")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])
      self.tokenizer._switch_to_input_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])

      a						:		List[Any]      							=			"zh"
      self.tokenizer._switch_to_target_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id("zh")])
      self.assertListEqual(self.tokenizer.suffix_tokens				, [self.tokenizer.eos_token_id])
      self.tokenizer._switch_to_input_mode()
      self.assertListEqual(self.tokenizer.prefix_tokens				, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])






    @require_torch
    def 	__snake_case							(					self				:    Any):
      a						:		str      							=			self.tokenizer._build_translation_inputs("A test"				, return_tensors="pt"				, src_lang="en"				, tgt_lang="ar")

      self.assertEqual(
          nested_simplify(__UpperCAmelCase)				, {
              # en_XX, A, test, EOS
              "input_ids": [[128022, 58, 4183, 2]],
              "attention_mask": [[1, 1, 1, 1]],
              # ar_AR
              "forced_bos_token_id": 128006,
          }				, )







"""simple docstring"""




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 			UpperCamelCase__:





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=13						,__UpperCAmelCase=7						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=99						,__UpperCAmelCase=32						,__UpperCAmelCase=2						,__UpperCAmelCase=4						,__UpperCAmelCase=37						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.1						,__UpperCAmelCase=5_12						,__UpperCAmelCase=16						,__UpperCAmelCase=2						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=3						,__UpperCAmelCase=4						,__UpperCAmelCase=None						,__UpperCAmelCase=0						,)    -> Dict:
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							seq_length
													A__		   =							is_training
													A__		   =							use_input_mask
													A__		   =							use_token_type_ids
													A__		   =							use_labels
													A__		   =							vocab_size
													A__		   =							hidden_size
													A__		   =							num_hidden_layers
													A__		   =							num_attention_heads
													A__		   =							intermediate_size
													A__		   =							hidden_act
													A__		   =							hidden_dropout_prob
													A__		   =							attention_probs_dropout_prob
													A__		   =							max_position_embeddings
													A__		   =							type_vocab_size
													A__		   =							type_sequence_label_size
													A__		   =							initializer_range
													A__		   =							num_labels
													A__		   =							num_choices
													A__		   =							scope
													A__		   =							projection_dim





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							ids_tensor([self.batch_size, self.seq_length]						,self.vocab_size   )

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

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

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

													A__		   =							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=__UpperCAmelCase						,initializer_range=self.initializer_range						,)
													A__		   =							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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													A__		   =							TFDPRContextEncoder(config=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,token_type_ids=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase						,token_type_ids=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.pooler_output.shape						,(self.batch_size, self.projection_dim or self.hidden_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							TFDPRQuestionEncoder(config=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase						,token_type_ids=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase						,token_type_ids=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.pooler_output.shape						,(self.batch_size, self.projection_dim or self.hidden_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[int]:
													A__		   =							TFDPRReader(config=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase						,attention_mask=__UpperCAmelCase   )

													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  snake_case__		(				self   )    -> int:
													A__		   =							self.prepare_config_and_inputs()
													(
													    (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     
													)		   =							config_and_inputs
													A__		   =							{'input_ids': input_ids}
													return config, inputs_dict






@require_tf
class 			UpperCamelCase__(	__A						,  __A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Optional[int]  							=						(
						    (
						        TFDPRContextEncoder,
						        TFDPRQuestionEncoder,
						        TFDPRReader,
						    )
						    if is_tf_available()
						    else ()
						)
						lowerCAmelCase__     :  List[str]  							=						{'feature-extraction': TFDPRQuestionEncoder} if is_tf_available() else {}

						lowerCAmelCase__     :  Tuple  							=						False
						lowerCAmelCase__     :  Optional[int]  							=						False
						lowerCAmelCase__     :  List[str]  							=						False
						lowerCAmelCase__     :  int  							=						False
						lowerCAmelCase__     :  str  							=						False





						def  snake_case__		(				self   )    -> str:
													A__		   =							TFDPRModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,hidden_size=37   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													self.config_tester.run_common_tests()





						def  snake_case__		(				self   )    -> int:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_dpr_context_encoder(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_dpr_question_encoder(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_dpr_reader(*__UpperCAmelCase   )





						@slow
						def  snake_case__		(				self   )    -> int:
													for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				A__		   =							TFDPRContextEncoder.from_pretrained(__UpperCAmelCase   )
																				self.assertIsNotNone(__UpperCAmelCase   )

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

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

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






@require_tf
class 			UpperCamelCase__(	unittest.TestCase       ):





						@slow
						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'   )

													A__		   =							tf.constant(
													    [[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]]   )  # [CLS] hello, is my dog cute? [SEP]
													A__		   =							model(__UpperCAmelCase   )[0]  # embedding shape = (1, 768)
													# compare the actual values for a slice.
													A__		   =							tf.constant(
													    [
													        [
													            0.0_3_2_3_6_2_5_3,
													            0.1_2_7_5_3_3_3_5,
													            0.1_6_8_1_8_5_0_9,
													            0.0_0_2_7_9_7_8_6,
													            0.3_8_9_6_9_3_3,
													            0.2_4_2_6_4_9_4_5,
													            0.2_1_7_8_9_7_1,
													            -0.0_2_3_3_5_2_2_7,
													            -0.0_8_4_8_1_9_5_9,
													            -0.1_4_3_2_4_1_1_7,
													        ]
													    ]   )
													self.assertTrue(numpy.allclose(output[:, :10].numpy()						,expected_slice.numpy()						,atol=1e-4   )   )





