






"""simple docstring"""





import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin







class 						_a  (		unittest.TestCase							,	lowercase_       ):





   '''simple docstring'''






   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     load_tool("document-question-answering")
          self.tool.setup()
          lowercase__:							List[str]         =     load_tool("document-question-answering"					, remote=UpperCAmelCase_)





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          lowercase__:							str         =     load_dataset("hf-internal-testing/example-documents"					, split="test")
          lowercase__:							Any         =     dataset[0]["image"]

          lowercase__:							int         =     self.tool(UpperCAmelCase_					, "When is the coffee break?")
          self.assertEqual(UpperCAmelCase_					, "11-14 to 11:39 a.m.")





   def __lowercase							(       self)			->   Union[str, Any]:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     load_dataset("hf-internal-testing/example-documents"					, split="test")
          lowercase__:							Any         =     dataset[0]["image"]

          lowercase__:							Tuple         =     self.remote_tool(UpperCAmelCase_					, "When is the coffee break?")
          self.assertEqual(UpperCAmelCase_					, "11-14 to 11:39 a.m.")





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          lowercase__:							Dict         =     load_dataset("hf-internal-testing/example-documents"					, split="test")
          lowercase__:							Tuple         =     dataset[0]["image"]

          self.tool(document=UpperCAmelCase_					, question="When is the coffee break?")





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							Optional[int]         =     load_dataset("hf-internal-testing/example-documents"					, split="test")
          lowercase__:							List[str]         =     dataset[0]["image"]

          lowercase__:							Any         =     self.remote_tool(document=UpperCAmelCase_					, question="When is the coffee break?")
          self.assertEqual(UpperCAmelCase_					, "11-14 to 11:39 a.m.")














"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase					=     {
    """configuration_led""": ["""LED_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LEDConfig"""],
    """tokenization_led""": ["""LEDTokenizer"""],
}

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

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     [
      """LED_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LEDForConditionalGeneration""",
      """LEDForQuestionAnswering""",
      """LEDForSequenceClassification""",
      """LEDModel""",
      """LEDPreTrainedModel""",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase					=     ["""TFLEDForConditionalGeneration""", """TFLEDModel""", """TFLEDPreTrainedModel"""]


if TYPE_CHECKING:
  from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
  from .tokenization_led import LEDTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_led_fast import LEDTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_led import (
        LED_PRETRAINED_MODEL_ARCHIVE_LIST,
        LEDForConditionalGeneration,
        LEDForQuestionAnswering,
        LEDForSequenceClassification,
        LEDModel,
        LEDPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
  import sys

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






