



"""simple docstring"""
import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase  :						Optional[Any]	=       "▁"

UpperCAmelCase  :						Dict	=       get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
class       SCREAMING_SNAKE_CASE__       (							__UpperCAmelCase					,     unittest.TestCase      ):
    lowercase__          =      BertGenerationTokenizer
    lowercase__          =      False
    lowercase__          =      True

    def     _UpperCAmelCase		( self	: Tuple):







      """simple docstring"""







      super().setUp()

      lowercase_	    =       BertGenerationTokenizer(__lowerCAmelCase       ,     keep_accents=__lowerCAmelCase)
      tokenizer.save_pretrained(self.tmpdirname)

    def     _UpperCAmelCase		( self	: Any):







      """simple docstring"""







      lowercase_	    =       """<s>"""
      lowercase_	    =       1

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

    def     _UpperCAmelCase		( self	: Union[str, Any]):







      """simple docstring"""







      lowercase_	    =       list(self.get_tokenizer().get_vocab().keys())

      self.assertEqual(vocab_keys[0]       ,     """<unk>""")
      self.assertEqual(vocab_keys[1]       ,     """<s>""")
      self.assertEqual(vocab_keys[-1]       ,     """<pad>""")
      self.assertEqual(len(__lowerCAmelCase)       ,     1_0_0_2)

    def     _UpperCAmelCase		( self	: Any):







      """simple docstring"""







      self.assertEqual(self.get_tokenizer().vocab_size       ,     1_0_0_0)

    def     _UpperCAmelCase		( self	: Any):







      """simple docstring"""







      lowercase_	    =       BertGenerationTokenizer(__lowerCAmelCase       ,     keep_accents=__lowerCAmelCase)

      lowercase_	    =       tokenizer.tokenize("""This is a test""")
      self.assertListEqual(__lowerCAmelCase       ,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__lowerCAmelCase)       ,     [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]       ,     )

      lowercase_	    =       tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
      self.assertListEqual(
          __lowerCAmelCase       ,     [
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """9""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """é""",
              """.""",
          ]       ,     )
      lowercase_	    =       tokenizer.convert_tokens_to_ids(__lowerCAmelCase)
      self.assertListEqual(
          __lowerCAmelCase       ,     [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]       ,     )

      lowercase_	    =       tokenizer.convert_ids_to_tokens(__lowerCAmelCase)
      self.assertListEqual(
          __lowerCAmelCase       ,     [
              SPIECE_UNDERLINE + """I""",
              SPIECE_UNDERLINE + """was""",
              SPIECE_UNDERLINE + """b""",
              """or""",
              """n""",
              SPIECE_UNDERLINE + """in""",
              SPIECE_UNDERLINE + """""",
              """<unk>""",
              """2""",
              """0""",
              """0""",
              """0""",
              """,""",
              SPIECE_UNDERLINE + """and""",
              SPIECE_UNDERLINE + """this""",
              SPIECE_UNDERLINE + """is""",
              SPIECE_UNDERLINE + """f""",
              """al""",
              """s""",
              """<unk>""",
              """.""",
          ]       ,     )

    @cached_property
    def     _UpperCAmelCase		( self	: Optional[int]):







      """simple docstring"""







      return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""")

    @slow
    def     _UpperCAmelCase		( self	: Any):







      """simple docstring"""







      lowercase_	    =       """Hello World!"""
      lowercase_	    =       [1_8_5_3_6, 2_2_6_0, 1_0_1]

      self.assertListEqual(__lowerCAmelCase       ,     self.big_tokenizer.encode(__lowerCAmelCase))

    @slow
    def     _UpperCAmelCase		( self	: Union[str, Any]):







      """simple docstring"""







      lowercase_	    =       (
          """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
          """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
      )
      lowercase_	    =       [
          8_7_1,
          4_1_9,
          3_5_8,
          9_4_6,
          9_9_1,
          2_5_2_1,
          4_5_2,
          3_5_8,
          1_3_5_7,
          3_8_7,
          7_7_5_1,
          3_5_3_6,
          1_1_2,
          9_8_5,
          4_5_6,
          1_2_6,
          8_6_5,
          9_3_8,
          5_4_0_0,
          5_7_3_4,
          4_5_8,
          1_3_6_8,
          4_6_7,
          7_8_6,
          2_4_6_2,
          5_2_4_6,
          1_1_5_9,
          6_3_3,
          8_6_5,
          4_5_1_9,
          4_5_7,
          5_8_2,
          8_5_2,
          2_5_5_7,
          4_2_7,
          9_1_6,
          5_0_8,
          4_0_5,
          3_4_3_2_4,
          4_9_7,
          3_9_1,
          4_0_8,
          1_1_3_4_2,
          1_2_4_4,
          3_8_5,
          1_0_0,
          9_3_8,
          9_8_5,
          4_5_6,
          5_7_4,
          3_6_2,
          1_2_5_9_7,
          3_2_0_0,
          3_1_2_9,
          1_1_7_2,
      ]

      self.assertListEqual(__lowerCAmelCase       ,     self.big_tokenizer.encode(__lowerCAmelCase))

    @require_torch
    @slow
    def     _UpperCAmelCase		( self	: List[str]):







      """simple docstring"""







      import torch

      from transformers import BertGenerationConfig, BertGenerationEncoder

      # Build sequence
      lowercase_	    =       list(self.big_tokenizer.get_vocab().keys())[:1_0]
      lowercase_	    =       """ """.join(__lowerCAmelCase)
      lowercase_	    =       self.big_tokenizer.encode_plus(__lowerCAmelCase       ,     return_tensors="""pt"""       ,     return_token_type_ids=__lowerCAmelCase)
      lowercase_	    =       self.big_tokenizer.batch_encode_plus(
          [sequence + """ """ + sequence]       ,     return_tensors="""pt"""       ,     return_token_type_ids=__lowerCAmelCase)

      lowercase_	    =       BertGenerationConfig()
      lowercase_	    =       BertGenerationEncoder(__lowerCAmelCase)

      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
        model(**__lowerCAmelCase)
        model(**__lowerCAmelCase)




    @slow
    def     _UpperCAmelCase		( self	: List[str]):







      """simple docstring"""







      lowercase_	    =       {"""input_ids""": [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 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], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 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]], """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, 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, 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=__lowerCAmelCase       ,     model_name="""google/bert_for_seq_generation_L-24_bbc_encoder"""       ,     revision="""c817d1fd1be2ffa69431227a1fe320544943d4db"""       ,     )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a 						=      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      ["LxmertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "LxmertEncoder",
			    "LxmertForPreTraining",
			    "LxmertForQuestionAnswering",
			    "LxmertModel",
			    "LxmertPreTrainedModel",
			    "LxmertVisualFeatureEncoder",
			    "LxmertXLayer",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFLxmertForPreTraining",
			    "TFLxmertMainLayer",
			    "TFLxmertModel",
			    "TFLxmertPreTrainedModel",
			    "TFLxmertVisualFeatureEncoder",
			]


if TYPE_CHECKING:
			from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
			from .tokenization_lxmert import LxmertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_lxmert_fast import LxmertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_lxmert import (
						    LxmertEncoder,
						    LxmertForPreTraining,
						    LxmertForQuestionAnswering,
						    LxmertModel,
						    LxmertPreTrainedModel,
						    LxmertVisualFeatureEncoder,
						    LxmertXLayer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_lxmert import (
						    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLxmertForPreTraining,
						    TFLxmertMainLayer,
						    TFLxmertModel,
						    TFLxmertPreTrainedModel,
						    TFLxmertVisualFeatureEncoder,
						)

else:
			import sys

			_a 						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



