




"""simple docstring"""


from typing import TYPE_CHECKING

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


A_						=				{
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_						=				["LayoutLMv2TokenizerFast"]

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_						=				["LayoutLMv2FeatureExtractor"]
     A_						=				["LayoutLMv2ImageProcessor"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A_						=				[
         "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
         "LayoutLMv2ForQuestionAnswering",
         "LayoutLMv2ForSequenceClassification",
         "LayoutLMv2ForTokenClassification",
         "LayoutLMv2Layer",
         "LayoutLMv2Model",
         "LayoutLMv2PreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
     from .processing_layoutlmva import LayoutLMvaProcessor
     from .tokenization_layoutlmva import LayoutLMvaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_layoutlmva import (
              LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              LayoutLMvaForQuestionAnswering,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaLayer,
              LayoutLMvaModel,
              LayoutLMvaPreTrainedModel,
          )
else:
     import sys

     A_						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class     __snake_case       (							unittest.TestCase   ,			__lowerCamelCase			):





       '''simple docstring'''







       def 							UpperCAmelCase__ (  self :    Union[str, Any]		):
         __snake_case:						Optional[int]   =						load_tool("""text-classification"""		)
         self.tool.setup()
         __snake_case:						Dict   =						load_tool("""text-classification"""							,				remote=A		)




       def 							UpperCAmelCase__ (  self :    Optional[int]		):
         __snake_case:						List[str]   =						self.tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         __snake_case:						List[str]   =						self.remote_tool("""That's quite cool"""							,				["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[Any]   =						self.tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






       def 							UpperCAmelCase__ (  self :    Tuple		):
         __snake_case:						int   =						self.remote_tool(text="""That's quite cool"""							,				labels=["""positive""", """negative"""]		)
         self.assertEqual(A							,				"""positive"""		)






