





'''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


lowerCAmelCase :					List[Any] 							=['''text''', '''image''', '''audio''']
def 							UpperCAmelCase_  (						__lowerCamelCase : List[str]			):
   lowercase_      :Optional[Any]         =			[]

   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_12, 5_12)			)			)
      elif input_type == "audio":
         inputs.append(torch.ones(30_00			)			)
      elif isinstance(_a     ,_a			):
         inputs.append(create_inputs(_a			)			)
      else:
         raise ValueError(F'Invalid type requested: {input_type}'			)

   return inputs
def 							UpperCAmelCase_  (						__lowerCamelCase : List			):
   lowercase_      :str         =			[]

   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 				a_       :





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




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

        lowercase_      :int         =			self.tool.inputs
        for _input in inputs:
           if isinstance(_input     ,				_SCREAMING_SNAKE_CASE						):
              for __input in _input:
                 self.assertTrue(__input in authorized_types						)
           else:
              self.assertTrue(_input in authorized_types						)

        lowercase_      :Dict         =			self.tool.outputs
        for _output in outputs:
           self.assertTrue(_output in authorized_types						)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			create_inputs(self.tool.inputs						)
        lowercase_      :Optional[int]         =			self.tool(*_SCREAMING_SNAKE_CASE						)

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

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





     def 							lowercase__				(			self		: int						):





        """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 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        lowercase_      :Dict         =			create_inputs(self.tool.inputs						)
        lowercase_      :Optional[int]         =			self.tool(*_SCREAMING_SNAKE_CASE						)

        if not isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE						):
           lowercase_      :List[Any]         =			[outputs]

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

        for output, output_type in zip(_SCREAMING_SNAKE_CASE     ,				self.tool.outputs						):
           lowercase_      :str         =			AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE						)						)







     def 							lowercase__				(			self		: str						):





        """simple docstring"""




        lowercase_      :Optional[int]         =			create_inputs(self.tool.inputs						)

        lowercase_      :List[Any]         =			[]

        for _input, input_type in zip(_SCREAMING_SNAKE_CASE     ,				self.tool.inputs						):
           if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE						):
              _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
        lowercase_      :Optional[Any]         =			self.tool(*_SCREAMING_SNAKE_CASE						)

        if not isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE						):
           lowercase_      :Any         =			[outputs]

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









'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase :					Optional[int] 							={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase :					Optional[Any] 							=[
     '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''WavLMForAudioFrameClassification''',
     '''WavLMForCTC''',
     '''WavLMForSequenceClassification''',
     '''WavLMForXVector''',
     '''WavLMModel''',
     '''WavLMPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavlm import (
      WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavLMForAudioFrameClassification,
      WavLMForCTC,
      WavLMForSequenceClassification,
      WavLMForXVector,
      WavLMModel,
      WavLMPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase :					Union[str, Any] 							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


