





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


_A :   Optional[Any]  			=  ['text', 'image', 'audio']



def 	_a						(   UpperCAmelCase					)    ->    Union[str, Any]:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							[]

   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((512, 512)					)					)
      elif input_type == "audio":
         inputs.append(torch.ones(3000					)					)
      elif isinstance(a__      ,  a__					):
         inputs.append(create_inputs(a__					)					)
      else:
         raise ValueError(f"Invalid type requested: {input_type}"					)

   return inputs



def 	_a						(   UpperCAmelCase					)    ->    Any:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[]

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

   return output_types




@is_tool_test
class        __SCREAMING_SNAKE_CASE      :







       def 						__lowerCamelCase   (				self					:     str      )       ->Tuple:
          self.assertTrue(hasattr(self.tool		, '''inputs'''      )      )
          self.assertTrue(hasattr(self.tool		, '''outputs'''      )      )

          lowerCamelCase__       :						Optional[Any]        =							self.tool.inputs
          for _input in inputs:
             if isinstance(_input		, __UpperCamelCase      ):
                for __input in _input:
                   self.assertTrue(__input in authorized_types      )
             else:
                self.assertTrue(_input in authorized_types      )

          lowerCamelCase__       :						Any        =							self.tool.outputs
          for _output in outputs:
             self.assertTrue(_output in authorized_types      )







       def 						__lowerCamelCase   (				self					:     str      )       ->int:
          lowerCamelCase__       :						Optional[Any]        =							create_inputs(self.tool.inputs      )
          lowerCamelCase__       :						List[Any]        =							self.tool(*__UpperCamelCase      )

          # There is a single output
          if len(self.tool.outputs      ) == 1:
             lowerCamelCase__       :						int        =							[outputs]

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







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->Dict:
          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 						__lowerCamelCase   (				self					:     str      )       ->Tuple:
          lowerCamelCase__       :						List[str]        =							create_inputs(self.tool.inputs      )
          lowerCamelCase__       :						Dict        =							self.tool(*__UpperCamelCase      )

          if not isinstance(__UpperCamelCase		, __UpperCamelCase      ):
             lowerCamelCase__       :						int        =							[outputs]

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

          for output, output_type in zip(__UpperCamelCase		, self.tool.outputs      ):
             lowerCamelCase__       :						Union[str, Any]        =							AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(__UpperCamelCase		, __UpperCamelCase      )      )







       def 						__lowerCamelCase   (				self					:     str      )       ->int:
          lowerCamelCase__       :						List[Any]        =							create_inputs(self.tool.inputs      )

          lowerCamelCase__       :						List[Any]        =							[]

          for _input, input_type in zip(__UpperCamelCase		, self.tool.inputs      ):
             if isinstance(__UpperCamelCase		, __UpperCamelCase      ):
                _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__       :						int        =							self.tool(*__UpperCamelCase      )

          if not isinstance(__UpperCamelCase		, __UpperCamelCase      ):
             lowerCamelCase__       :						Tuple        =							[outputs]

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










import os
from pathlib import Path



def 	_a						(   )    ->    Tuple:







   """simple docstring"""
   from torch.utils.cpp_extension import load

   lowerCamelCase__       :						List[Any]        =							Path(UpperCAmelCase					).resolve().parent.parent.parent / '''kernels''' / '''deformable_detr'''
   lowerCamelCase__       :						Any        =							[
       root / filename
       for filename in [
           '''vision.cpp''',
           os.path.join('''cpu'''      ,  '''ms_deform_attn_cpu.cpp'''					),
           os.path.join('''cuda'''      ,  '''ms_deform_attn_cuda.cu'''					),
       ]
   ]

   load(
       '''MultiScaleDeformableAttention'''      ,  UpperCAmelCase      ,  with_cuda=UpperCAmelCase      ,  extra_include_paths=[str(UpperCAmelCase					)]      ,  extra_cflags=['''-DWITH_CUDA=1''']      ,  extra_cuda_cflags=[
           '''-DCUDA_HAS_FP16=1''',
           '''-D__CUDA_NO_HALF_OPERATORS__''',
           '''-D__CUDA_NO_HALF_CONVERSIONS__''',
           '''-D__CUDA_NO_HALF2_OPERATORS__''',
       ]      ,  )

   import MultiScaleDeformableAttention as MSDA

   return MSDA



