






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


def        _a  (     UpperCamelCase_					:				List[str]   ) ->   Optional[int]:
						"""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        _a  (     UpperCamelCase_					:				List   ) ->   Tuple:
						"""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      lowercase__     :

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





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

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

	def 		UpperCAmelCase  (							self       )->					Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						create_inputs(self.tool.inputs       )
							lowerCAmelCase__       =						self.tool(*__UpperCAmelCase       )

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

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

	def 		UpperCAmelCase  (							self       )->					List[Any]:







							'''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 		UpperCAmelCase  (							self       )->					List[Any]:







							'''simple docstring'''





							lowerCAmelCase__       =						create_inputs(self.tool.inputs       )
							lowerCAmelCase__       =						self.tool(*__UpperCAmelCase       )

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

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

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


	def 		UpperCAmelCase  (							self       )->					int:







							'''simple docstring'''





							lowerCAmelCase__       =						create_inputs(self.tool.inputs       )

							lowerCAmelCase__       =						[]

							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__       =						self.tool(*__UpperCAmelCase       )

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

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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SEWForCTC''',
	    '''SEWForSequenceClassification''',
	    '''SEWModel''',
	    '''SEWPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew import (
		    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWForCTC,
		    SEWForSequenceClassification,
		    SEWModel,
		    SEWPreTrainedModel,
		)


else:
	import sys

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






