'''simple docstring'''





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   UpperCamelCase					(       unittest.TestCase    ,     a_       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any):


         """simple docstring"""







         a					:							Dict							= load_tool('image-segmentation')
         self.tool.setup()
         a					:							Optional[Any]							= load_tool('image-segmentation'							,							remote=UpperCAmelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a					:							Tuple							= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
         a					:							Optional[Any]							= self.tool(UpperCAmelCase_							,							'cat')
         self.assertTrue(isinstance(UpperCAmelCase_							,							Image.Image))





    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         a					:							List[str]							= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
         a					:							Optional[Any]							= self.remote_tool(UpperCAmelCase_							,							'cat')
         self.assertTrue(isinstance(UpperCAmelCase_							,							Image.Image))





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







         a					:							int							= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
         a					:							Union[str, Any]							= self.tool(image=UpperCAmelCase_							,							label='cat')
         self.assertTrue(isinstance(UpperCAmelCase_							,							Image.Image))







    def    SCREAMING_SNAKE_CASE_     (					self     :						str):


         """simple docstring"""







         a					:							Optional[Any]							= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO')) / '000000039769.png')
         a					:							Dict							= self.remote_tool(image=UpperCAmelCase_							,							label='cat')
         self.assertTrue(isinstance(UpperCAmelCase_							,							Image.Image))


'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase       :     Tuple			=    {
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     int			=    ["""MarkupLMTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase       :     Any			=    [
          """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MarkupLMForQuestionAnswering""",
          """MarkupLMForSequenceClassification""",
          """MarkupLMForTokenClassification""",
          """MarkupLMModel""",
          """MarkupLMPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
      from .feature_extraction_markuplm import MarkupLMFeatureExtractor
      from .processing_markuplm import MarkupLMProcessor
      from .tokenization_markuplm import MarkupLMTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_markuplm_fast import MarkupLMTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_markuplm import (
                MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                MarkupLMForQuestionAnswering,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMModel,
                MarkupLMPreTrainedModel,
            )


else:
      import sys

      UpperCamelCase       :     Dict			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure)

