





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


A__:    Any  	=	'''▁'''

A__:    str  	=	get_tests_dir('''fixtures/test_sentencepiece.model''')




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

      """simple docstring"""


      UpperCamelCase__  		=       BertGenerationTokenizer
      UpperCamelCase__  		=       False
      UpperCamelCase__  		=       True






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          super().setUp()

          UpperCamelCase__:	Dict					=     BertGenerationTokenizer(__lowerCamelCase	,    keep_accents=__lowerCamelCase					)
          tokenizer.save_pretrained(self.tmpdirname					)






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     "<s>"
          UpperCamelCase__:	Dict					=     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:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     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					)	,    1002					)






      def 			UpperCAmelCase_       (		self:			Union[str, Any]					):
          '''simple docstring'''


          self.assertEqual(self.get_tokenizer().vocab_size	,    1000					)






      def 			UpperCAmelCase_       (		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     BertGenerationTokenizer(__lowerCamelCase	,    keep_accents=__lowerCamelCase					)

          UpperCamelCase__:	Optional[Any]					=     tokenizer.tokenize("This is a test"					)
          self.assertListEqual(__lowerCamelCase	,    ["▁This", "▁is", "▁a", "▁t", "est"]					)

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(__lowerCamelCase					)	,    [285, 46, 10, 170, 382]	,    )

          UpperCamelCase__:	Dict					=     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",
                  ".",
              ]	,    )
          UpperCamelCase__:	List[Any]					=     tokenizer.convert_tokens_to_ids(__lowerCamelCase					)
          self.assertListEqual(
              __lowerCamelCase	,    [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]	,    )

          UpperCamelCase__:	str					=     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:			List[Any]					):
          '''simple docstring'''


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






      @slow
      def 			UpperCAmelCase_       (		self:			Optional[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     "Hello World!"
          UpperCamelCase__:	Any					=     [1_8536, 2260, 101]

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






      @slow
      def 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	Tuple					=     (
              "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"
          )
          UpperCamelCase__:	int					=     [
              871,
              419,
              358,
              946,
              991,
              2521,
              452,
              358,
              1357,
              387,
              7751,
              3536,
              112,
              985,
              456,
              126,
              865,
              938,
              5400,
              5734,
              458,
              1368,
              467,
              786,
              2462,
              5246,
              1159,
              633,
              865,
              4519,
              457,
              582,
              852,
              2557,
              427,
              916,
              508,
              405,
              3_4324,
              497,
              391,
              408,
              1_1342,
              1244,
              385,
              100,
              938,
              985,
              456,
              574,
              362,
              1_2597,
              3200,
              3129,
              1172,
          ]

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






      @require_torch
      @slow
      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          import torch

          from transformers import BertGenerationConfig, BertGenerationEncoder

          # Build sequence
          UpperCamelCase__:	Tuple					=     list(self.big_tokenizer.get_vocab().keys()					)[:10]
          UpperCamelCase__:	Dict					=     " ".join(__lowerCamelCase					)
          UpperCamelCase__:	Dict					=     self.big_tokenizer.encode_plus(__lowerCamelCase	,    return_tensors="pt"	,    return_token_type_ids=__lowerCamelCase					)
          UpperCamelCase__:	Union[str, Any]					=     self.big_tokenizer.batch_encode_plus(
              [sequence + " " + sequence]	,    return_tensors="pt"	,    return_token_type_ids=__lowerCamelCase					)

          UpperCamelCase__:	Dict					=     BertGenerationConfig()
          UpperCamelCase__:	Optional[int]					=     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[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     {"input_ids": [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__:    List[Any]  	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''OPTForCausalLM''',
       '''OPTModel''',
       '''OPTPreTrainedModel''',
       '''OPTForSequenceClassification''',
       '''OPTForQuestionAnswering''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]  	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    str  	=	[
       '''FlaxOPTForCausalLM''',
       '''FlaxOPTModel''',
       '''FlaxOPTPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_opt import (
          OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OPTForCausalLM,
          OPTForQuestionAnswering,
          OPTForSequenceClassification,
          OPTModel,
          OPTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
   import sys

   A__:    Optional[Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

