






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  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	RoFormerTokenizer
     UpperCamelCase_							:		Dict			=	RoFormerTokenizerFast
     UpperCamelCase_							:		int			=	True
     UpperCamelCase_							:		str			=	True


     def 				_A       (				self       :	Tuple      ):
            super().setUp()


     def 				_A       (				self       :	Any					, **UpperCAmelCase_       :	Optional[int]      ):
            return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"					, **UpperCAmelCase_      )


     def 				_A       (				self       :	int					, **UpperCAmelCase_       :	str      ):
            return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"					, **UpperCAmelCase_      )


     def 				_A       (				self       :	Dict      ):
            SCREAMING_SNAKE_CASE       :      Any							=    "永和服装饰品有限公司,今天天气非常好"
            SCREAMING_SNAKE_CASE       :      Any							=    "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
            return input_text, output_text


     def 				_A       (				self       :	Tuple      ):
            SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.get_tokenizer()
            SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.get_chinese_input_output_texts()
            SCREAMING_SNAKE_CASE       :      int							=    tokenizer.tokenize(UpperCAmelCase_      )

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

            SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tokens + [tokenizer.unk_token]
            SCREAMING_SNAKE_CASE       :      str							=    [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )					, UpperCAmelCase_      )


     def 				_A       (				self       :	Optional[Any]      ):
            SCREAMING_SNAKE_CASE       :      List[Any]							=    self.get_rust_tokenizer()
            SCREAMING_SNAKE_CASE       :      str							=    self.get_chinese_input_output_texts()
            SCREAMING_SNAKE_CASE       :      Tuple							=    tokenizer.tokenize(UpperCAmelCase_      )
            self.assertListEqual(UpperCAmelCase_					, output_text.split()      )
            SCREAMING_SNAKE_CASE       :      str							=    tokens + [tokenizer.unk_token]
            SCREAMING_SNAKE_CASE       :      int							=    [2_2943, 2_1332, 3_4431, 4_5904, 117, 306, 1231, 1231, 2653, 3_3994, 1266, 100]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )					, UpperCAmelCase_      )


     def 				_A       (				self       :	List[str]      ):
            pass


     def 				_A       (				self       :	Any      ):
            pass






     def 				_A       (				self       :	int      ):
            pass














from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

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






