

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      				=     ['''text''', '''image''', '''audio''']


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]

					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(3_000      )      )
										elif isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
															inputs.append(create_inputs(UpperCAmelCase__      )      )
										else:
															raise ValueError(F"""Invalid type requested: {input_type}"""      )

					return inputs


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							[]

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

					return output_types





@is_tool_test
class     snake_case_			:


							"""simple docstring"""





							def        A__  ( self							):
												self.assertTrue(hasattr(self.tool					,						'inputs'							)							)
												self.assertTrue(hasattr(self.tool					,						'outputs'							)							)

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




							def        A__  ( self							):
												__lowerCAmelCase					=							create_inputs(self.tool.inputs							)
												__lowerCAmelCase					=							self.tool(*_A							)

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

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




							def        A__  ( self							):
												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							):
												__lowerCAmelCase					=							create_inputs(self.tool.inputs							)
												__lowerCAmelCase					=							self.tool(*_A							)

												if not isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							[outputs]

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

												for output, output_type in zip(_A					,						self.tool.outputs							):
																	__lowerCAmelCase					=							AGENT_TYPE_MAPPING[output_type]
																	self.assertTrue(isinstance(_A					,						_A							)							)




							def        A__  ( self							):
												__lowerCAmelCase					=							create_inputs(self.tool.inputs							)

												__lowerCAmelCase					=							[]

												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					=							self.tool(*_A							)

												if not isinstance(_A					,						_A							):
																	__lowerCAmelCase					=							[outputs]

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









from typing import TYPE_CHECKING

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


lowerCamelCase      				=     {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''UniSpeechForCTC''',
		    '''UniSpeechForPreTraining''',
		    '''UniSpeechForSequenceClassification''',
		    '''UniSpeechModel''',
		    '''UniSpeechPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech import (
				    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechForCTC,
				    UniSpeechForPreTraining,
				    UniSpeechForSequenceClassification,
				    UniSpeechModel,
				    UniSpeechPreTrainedModel,
				)

else:
		import sys

		lowerCamelCase      				=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






