





from typing import TYPE_CHECKING

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


lowercase     :			Tuple     						=	{
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			Any     						=	["""LayoutLMTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			Optional[int]     						=	[
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase     :			Optional[int]     						=	[
           """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLayoutLMForMaskedLM""",
           """TFLayoutLMForSequenceClassification""",
           """TFLayoutLMForTokenClassification""",
           """TFLayoutLMForQuestionAnswering""",
           """TFLayoutLMMainLayer""",
           """TFLayoutLMModel""",
           """TFLayoutLMPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
       from .tokenization_layoutlm import LayoutLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlm import (
                  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMForMaskedLM,
                  LayoutLMForQuestionAnswering,
                  LayoutLMForSequenceClassification,
                  LayoutLMForTokenClassification,
                  LayoutLMModel,
                  LayoutLMPreTrainedModel,
              )
       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlm import (
                  TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMForMaskedLM,
                  TFLayoutLMForQuestionAnswering,
                  TFLayoutLMForSequenceClassification,
                  TFLayoutLMForTokenClassification,
                  TFLayoutLMMainLayer,
                  TFLayoutLMModel,
                  TFLayoutLMPreTrainedModel,
              )

else:
       import sys

       lowercase     :			Dict     						=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
       from PIL import Image

class 			__A(    unittest.TestCase    ,      __UpperCAmelCase			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    load_tool('''image-question-answering'''   )
            self.tool.setup()
            _UpperCamelCase														=    load_tool('''image-question-answering''',							remote=A   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(A,							'''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''   )   ) / '''000000039769.png'''   )
            _UpperCamelCase														=    self.remote_tool(image=A,							question='''How many cats are sleeping on the couch?'''   )
            self.assertEqual(A,							'''2'''   )






