




import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase       :       Any        =    get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class 						__lowercase		(UpperCAmelCase__     ,		unittest.TestCase							):




      """simple docstring"""





      _snake_case							     =						ReformerTokenizer
      _snake_case							     =						ReformerTokenizerFast
      _snake_case							     =						True
      _snake_case							     =						False
      _snake_case							     =						True

      def 	UpperCAmelCase      (      self	)		->				Any:
         super().setUp()

         snake_case      :				Union[str, Any]				=       ReformerTokenizer(A				,      keep_accents=A	)
         tokenizer.save_pretrained(self.tmpdirname	)

      def 	UpperCAmelCase      (      self	)		->				Dict:
         snake_case      :				List[str]				=       """<s>"""
         snake_case      :				List[str]				=       1

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

      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         snake_case      :				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]				,      """j"""	)
         self.assertEqual(len(A	)				,      1_0_0_0	)

      def 	UpperCAmelCase      (      self	)		->				Tuple:
         self.assertEqual(self.get_tokenizer().vocab_size				,      1_0_0_0	)

      def 	UpperCAmelCase      (      self	)		->				Optional[Any]:
         if not self.test_rust_tokenizer:
            return

         snake_case      :				Tuple				=       self.get_tokenizer()
         snake_case      :				List[str]				=       self.get_rust_tokenizer()

         snake_case      :				List[Any]				=       """I was born in 92000, and this is falsé."""

         snake_case      :				Union[str, Any]				=       tokenizer.tokenize(A	)
         snake_case      :				str				=       rust_tokenizer.tokenize(A	)
         self.assertListEqual(A				,      A	)

         snake_case      :				Dict				=       tokenizer.encode(A				,      add_special_tokens=A	)
         snake_case      :				Union[str, Any]				=       rust_tokenizer.encode(A				,      add_special_tokens=A	)
         self.assertListEqual(A				,      A	)

         snake_case      :				str				=       self.get_rust_tokenizer()
         snake_case      :				int				=       tokenizer.encode(A	)
         snake_case      :				Optional[int]				=       rust_tokenizer.encode(A	)
         self.assertListEqual(A				,      A	)

      def 	UpperCAmelCase      (      self				,      A=1_5	)		->				Union[str, Any]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
            with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
               snake_case      :				Dict				=       self.rust_tokenizer_class.from_pretrained(A				,      **A	)

               # Simple input
               snake_case      :				List[Any]				=       """This is a simple input"""
               snake_case      :				Union[str, Any]				=       ["""This is a simple input 1""", """This is a simple input 2"""]
               snake_case      :				Tuple				=       ("""This is a simple input""", """This is a pair""")
               snake_case      :				str				=       [
                   ("""This is a simple input 1""", """This is a simple input 2"""),
                   ("""This is a simple pair 1""", """This is a simple pair 2"""),
               ]

               # Simple input tests
               self.assertRaises(A				,      tokenizer_r.encode				,      A				,      max_length=A				,      padding="""max_length"""	)

               # Simple input
               self.assertRaises(A				,      tokenizer_r.encode_plus				,      A				,      max_length=A				,      padding="""max_length"""	)

               # Simple input
               self.assertRaises(
                   A				,      tokenizer_r.batch_encode_plus				,      A				,      max_length=A				,      padding="""max_length"""				,      )

               # Pair input
               self.assertRaises(A				,      tokenizer_r.encode				,      A				,      max_length=A				,      padding="""max_length"""	)

               # Pair input
               self.assertRaises(A				,      tokenizer_r.encode_plus				,      A				,      max_length=A				,      padding="""max_length"""	)

               # Pair input
               self.assertRaises(
                   A				,      tokenizer_r.batch_encode_plus				,      A				,      max_length=A				,      padding="""max_length"""				,      )

      def 	UpperCAmelCase      (      self	)		->				Optional[int]:
         pass

      def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
         snake_case      :				List[str]				=       ReformerTokenizer(A				,      keep_accents=A	)

         snake_case      :				Union[str, Any]				=       tokenizer.tokenize("""This is a test"""	)
         self.assertListEqual(A				,      ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]	)

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

         snake_case      :				Tuple				=       tokenizer.tokenize("""I was born in 92000, and this is falsé."""	)
         self.assertListEqual(
             A				,      [
                 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""",
                 """.""",
             ]				,      )
         snake_case      :				List[Any]				=       tokenizer.convert_tokens_to_ids(A	)
         self.assertListEqual(
             A				,      [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]				,      )

         snake_case      :				Union[str, Any]				=       tokenizer.convert_ids_to_tokens(A	)
         self.assertListEqual(
             A				,      [
                 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[str]:
         return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""	)

      @slow
      def 	UpperCAmelCase      (      self	)		->				int:
         snake_case      :				Any				=       """Hello World!"""
         snake_case      :				List[str]				=       [1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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

      @slow
      def 	UpperCAmelCase      (      self	)		->				List[Any]:
         snake_case      :				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"""
         )
         snake_case      :				Optional[Any]				=       [
             1_0_8,
             2_6_5,
             2_4,
             1_1_1,
             4,
             2_5_8,
             1_5_6,
             3_5,
             2_8,
             2_7_5,
             3,
             2_5_9,
             2_9_7,
             2_6_0,
             8_4,
             4,
             3_5,
             1_1_0,
             4_4,
             8,
             2_5_9,
             9_1,
             2_6_8,
             2_1,
             1_1,
             2_0_9,
             2_7_4,
             1_0_9,
             2_6_6,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             3_1_5,
             2_5_8,
             2_7_8,
             2_5_8,
             2_7_7,
             2_5_8,
             0,
             2_5_8,
             2_8_8,
             2_5_8,
             3_1_9,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             2_8_7,
             2_5_8,
             3_1_5,
             2_5_8,
             2_8_9,
             2_5_8,
             2_7_8,
             9_9,
             2_6_9,
             2_6_6,
             2_6_2,
             8,
             2_5_9,
             2_4_1,
             4,
             2_1_7,
             2_3_0,
             2_6_8,
             2_6_6,
             5_5,
             1_6_8,
             1_0_6,
             7_5,
             1_9_3,
             2_6_6,
             2_2_3,
             2_7,
             4_9,
             2_6,
             2_8_2,
             2_5,
             2_6_4,
             2_9_9,
             1_9,
             2_6,
             0,
             2_5_8,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             1_7_6,
             1_8_3,
             2_7_0,
             1_1,
             2_6_2,
             4_2,
             6_1,
             2_6_5,
         ]

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

      @require_torch
      @slow
      def 	UpperCAmelCase      (      self	)		->				List[str]:
         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         snake_case      :				Dict				=       list(self.big_tokenizer.get_vocab().keys()	)[:1_0]
         snake_case      :				Dict				=       """ """.join(A	)
         snake_case      :				Optional[int]				=       self.big_tokenizer.encode_plus(A				,      return_tensors="""pt"""	)
         snake_case      :				Optional[int]				=       self.big_tokenizer.batch_encode_plus([sequence, sequence]				,      return_tensors="""pt"""	)

         snake_case      :				str				=       ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         snake_case      :				Union[str, Any]				=       encoded_sequence["""input_ids"""].shape
         snake_case      :				List[str]				=       ReformerModel(A	)

         # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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

      @slow
      def 	UpperCAmelCase      (      self	)		->				str:
         snake_case      :				Any				=       {"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """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]]}  # noqa: E501
         # fmt: on

         # This tokenizer does not know some characters like ")".
         # That is the reason why we use very simple texts here.
         # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
         snake_case      :				str				=       [
             """This is a very simple sentence.""",
             """The quick brown fox jumps over the lazy dog.""",
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=A				,      model_name="""google/reformer-crime-and-punishment"""				,      revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""				,      padding=A				,      sequences=A				,      )









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__:    List[str]					    =       {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]					    =       ['''ChineseCLIPFeatureExtractor''']
   A__:    Any					    =       ['''ChineseCLIPImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict					    =       [
       '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ChineseCLIPModel''',
       '''ChineseCLIPPreTrainedModel''',
       '''ChineseCLIPTextModel''',
       '''ChineseCLIPVisionModel''',
   ]

if TYPE_CHECKING:
   from .configuration_chinese_clip import (
       CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       ChineseCLIPConfig,
       ChineseCLIPOnnxConfig,
       ChineseCLIPTextConfig,
       ChineseCLIPVisionConfig,
   )
   from .processing_chinese_clip import ChineseCLIPProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_chinese_clip import (
          CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ChineseCLIPModel,
          ChineseCLIPPreTrainedModel,
          ChineseCLIPTextModel,
          ChineseCLIPVisionModel,
      )

else:
   import sys

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


