




from typing import TYPE_CHECKING

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


lowercase__				:      Optional[int]	   = {
    "configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnxConfig"]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				:      str	   = ["ConvNextFeatureExtractor"]
   lowercase__				:      List[str]	   = ["ConvNextImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				:      Optional[Any]	   = [
       "CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ConvNextForImageClassification",
       "ConvNextModel",
       "ConvNextPreTrainedModel",
       "ConvNextBackbone",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowercase__				:      List[str]	   = [
       "TFConvNextForImageClassification",
       "TFConvNextModel",
       "TFConvNextPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_convnext import ConvNextFeatureExtractor
      from .image_processing_convnext import ConvNextImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convnext import (
          CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvNextBackbone,
          ConvNextForImageClassification,
          ConvNextModel,
          ConvNextPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
   import sys

   lowercase__				:      str	   = _LazyModule(__name__, globals()["__file__"], _import_structure)







import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_rjieba
@require_tokenizers
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     RoFormerTokenizer
     a		:      Tuple		 =     RoFormerTokenizerFast
     a		:      Dict		 =     True
     a		:      Optional[Any]		 =     True







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          super().setUp()







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Tuple:



          '''simple docstring'''



          return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				"永和服装饰品有限公司,今天天气非常好"
          a     =				"永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
          return input_text, output_text







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)

          self.assertListEqual(A  ,		output_text.split()			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.get_rust_tokenizer()
          a  ,				a     =				self.get_chinese_input_output_texts()
          a     =				tokenizer.tokenize(A			)
          self.assertListEqual(A  ,		output_text.split()			)
          a     =				tokens + [tokenizer.unk_token]
          a     =				[22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          pass

