





"""simple docstring"""





from typing import TYPE_CHECKING

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


__A											=						{
    """configuration_layoutlmv3""": [
        """LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """LayoutLMv3Config""",
        """LayoutLMv3OnnxConfig""",
    ],
    """processing_layoutlmv3""": ["""LayoutLMv3Processor"""],
    """tokenization_layoutlmv3""": ["""LayoutLMv3Tokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""LayoutLMv3TokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """LayoutLMv3ForQuestionAnswering""",
      """LayoutLMv3ForSequenceClassification""",
      """LayoutLMv3ForTokenClassification""",
      """LayoutLMv3Model""",
      """LayoutLMv3PreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFLayoutLMv3ForQuestionAnswering""",
      """TFLayoutLMv3ForSequenceClassification""",
      """TFLayoutLMv3ForTokenClassification""",
      """TFLayoutLMv3Model""",
      """TFLayoutLMv3PreTrainedModel""",
  ]

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""LayoutLMv3FeatureExtractor"""]
  __A											=						["""LayoutLMv3ImageProcessor"""]


if TYPE_CHECKING:
  from .configuration_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
      LayoutLMvaConfig,
      LayoutLMvaOnnxConfig,
  )
  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_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlmva import (
        LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMvaForQuestionAnswering,
        LayoutLMvaForSequenceClassification,
        LayoutLMvaForTokenClassification,
        LayoutLMvaModel,
        LayoutLMvaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_layoutlmva import (
        TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLayoutLMvaForQuestionAnswering,
        TFLayoutLMvaForSequenceClassification,
        TFLayoutLMvaForTokenClassification,
        TFLayoutLMvaModel,
        TFLayoutLMvaPreTrainedModel,
    )

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

else:
  import sys

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


'''simple docstring'''

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


snake_case									=    ["""text""", """image""", """audio"""]


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

 for input_type in input_types:
  if input_type == "text":
   inputs.append("Text input"			)
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"			)			) / "000000039769.png"			).resize((5_1_2, 5_1_2)			)			)
  elif input_type == "audio":
   inputs.append(torch.ones(3_0_0_0			)			)
  elif isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
   inputs.append(create_inputs(lowerCamelCase_			)			)
  else:
   raise ValueError(f'''Invalid type requested: {input_type}'''			)

 return inputs


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   str     		=	[]

 for output in outputs:
  if isinstance(lowerCamelCase_      ,			(str, AgentText)			):
   output_types.append("text"			)
  elif isinstance(lowerCamelCase_      ,			(Image.Image, AgentImage)			):
   output_types.append("image"			)
  elif isinstance(lowerCamelCase_      ,			(torch.Tensor, AgentAudio)			):
   output_types.append("audio"			)
  else:
   raise ValueError(f'''Invalid output: {output}'''			)

 return output_types







@is_tool_test
class    lowerCAmelCase       :


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  self.assertTrue(hasattr(self.tool			,							"inputs"    )    )
  self.assertTrue(hasattr(self.tool			,							"outputs"    )    )

  lowerCAmelCase__     :   Dict     		=	self.tool.inputs
  for _input in inputs:
   if isinstance(_input			,							a__    ):
    for __input in _input:
     self.assertTrue(__input in authorized_types    )
   else:
    self.assertTrue(_input in authorized_types    )

  lowerCAmelCase__     :   List[Any]     		=	self.tool.outputs
  for _output in outputs:
   self.assertTrue(_output in authorized_types    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

  # There is a single output
  if len(self.tool.outputs    ) == 1:
   lowerCAmelCase__     :   Union[str, Any]     		=	[outputs]

  self.assertListEqual(output_types(a__    )			,							self.tool.outputs    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  self.assertTrue(hasattr(self.tool			,							"description"    )    )
  self.assertTrue(hasattr(self.tool			,							"default_checkpoint"    )    )
  self.assertTrue(self.tool.description.startswith("This is a tool that"    )    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	create_inputs(self.tool.inputs    )
  lowerCAmelCase__     :   str     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   List[str]     		=	[outputs]

  self.assertEqual(len(a__    )			,							len(self.tool.outputs    )    )

  for output, output_type in zip(a__			,							self.tool.outputs    ):
   lowerCAmelCase__     :   List[Any]     		=	AGENT_TYPE_MAPPING[output_type]
   self.assertTrue(isinstance(a__			,							a__    )    )


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   int     		=	create_inputs(self.tool.inputs    )

  lowerCAmelCase__     :   Dict     		=	[]

  for _input, input_type in zip(a__			,							self.tool.inputs    ):
   if isinstance(a__			,							a__    ):
    _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
   else:
    _inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
  lowerCAmelCase__     :   Any     		=	self.tool(*a__    )

  if not isinstance(a__			,							a__    ):
   lowerCAmelCase__     :   Tuple     		=	[outputs]

  self.assertEqual(len(a__    )			,							len(self.tool.outputs    )    )





