




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		MgpstrTokenizer
     _a											=		False
     _a											=		{}
     _a											=		False

     def       a__			(						self       )		->							str:
           super().setUp()

           # fmt: off
           _A							:  Optional[Any]    =		["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
           # fmt: on
           _A							:  List[str]    =		dict(zip(_a	,  range(len(_a       )       )       )       )

           _A							:  Union[str, Any]    =		os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["""vocab_file"""]       )
           with open(self.vocab_file	,  """w"""	,  encoding="""utf-8"""       ) as fp:
                 fp.write(json.dumps(_a       ) + """\n"""       )

     def       a__			(						self	,  **_a       )		->							Optional[Any]:
           return MgpstrTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           _A							:  str    =		"""tester"""
           _A							:  Union[str, Any]    =		"""tester"""
           return input_text, output_text

     @unittest.skip("""MGP-STR always lower cases letters."""       )
     def       a__			(						self       )		->							Optional[Any]:
           pass

     def       a__			(						self       )		->							Tuple:
           _A							:  Optional[Any]    =		self.get_tokenizers(do_lower_case=_a       )
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       _A							:  int    =		"""[SPECIAL_TOKEN]"""

                       tokenizer.add_special_tokens({"""cls_token""": special_token}       )
                       _A							:  Union[str, Any]    =		tokenizer.encode([special_token]	,  add_special_tokens=_a       )
                       self.assertEqual(len(_a       )	,  1       )

                       _A							:  Optional[int]    =		tokenizer.decode(_a	,  skip_special_tokens=_a       )
                       self.assertTrue(special_token not in decoded       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Dict    =		self.get_tokenizers()
           for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
                       _A							:  Any    =		self.get_input_output_texts(_a       )

                       _A							:  str    =		tokenizer.tokenize(_a       )
                       _A							:  List[Any]    =		tokenizer.convert_tokens_to_ids(_a       )
                       _A							:  Union[str, Any]    =		tokenizer.encode(_a	,  add_special_tokens=_a       )
                       self.assertListEqual(_a	,  _a       )

                       _A							:  List[Any]    =		tokenizer.convert_ids_to_tokens(_a       )
                       self.assertNotEqual(len(_a       )	,  0       )
                       _A							:  Optional[Any]    =		tokenizer.decode(_a       )
                       self.assertIsInstance(_a	,  _a       )

                       self.assertEqual(text_a.replace(""" """	,  """"""       )	,  _a       )

     @unittest.skip("""MGP-STR tokenizer only handles one sequence."""       )
     def       a__			(						self       )		->							Optional[int]:
           pass



     @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""       )
     def       a__			(						self       )		->							Any:
           pass







from typing import TYPE_CHECKING

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


_snake_case            = {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = ["EfficientFormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientFormerForImageClassification",
      "EfficientFormerForImageClassificationWithTeacher",
      "EfficientFormerModel",
      "EfficientFormerPreTrainedModel",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case            = [
      "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEfficientFormerForImageClassification",
      "TFEfficientFormerForImageClassificationWithTeacher",
      "TFEfficientFormerModel",
      "TFEfficientFormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientformer import EfficientFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientformer import (
        EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientFormerForImageClassification,
        EfficientFormerForImageClassificationWithTeacher,
        EfficientFormerModel,
        EfficientFormerPreTrainedModel,
    )
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_efficientformer import (
        TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEfficientFormerForImageClassification,
        TFEfficientFormerForImageClassificationWithTeacher,
        TFEfficientFormerModel,
        TFEfficientFormerPreTrainedModel,
    )

else:
  import sys

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

