


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





def lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







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

	return inputs





def lowerCamelCase      (      lowerCAmelCase    :     List		):





	"""simple docstring"""







	__magic_name__	:   Dict					     =					[]

	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	:



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[int]:
			self.assertTrue(hasattr(self.tool  ,					'inputs'      )      )
			self.assertTrue(hasattr(self.tool  ,					'outputs'      )      )

			__magic_name__	:   int					     =					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      )

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







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Any:
			__magic_name__	:   Optional[Any]					     =					create_inputs(self.tool.inputs      )
			__magic_name__	:   Dict					     =					self.tool(*_A      )

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

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







		def        __lowerCAmelCase		( self		:						str      )  ->			str:
			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		:						List[str]      )  ->			Optional[int]:
			__magic_name__	:   int					     =					create_inputs(self.tool.inputs      )
			__magic_name__	:   Optional[int]					     =					self.tool(*_A      )

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

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

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







		def        __lowerCAmelCase		( self		:						Dict      )  ->			int:
			__magic_name__	:   Dict					     =					create_inputs(self.tool.inputs      )

			__magic_name__	:   str					     =					[]

			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
			__magic_name__	:   str					     =					self.tool(*_A      )

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

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



'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Any  ,					*_A		:						List[Any]  ,					**_A		:						str      )  ->			None:
			warnings.warn(
			    'The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
			    ' use BeitImageProcessor instead.'  ,					_A  ,					)
			super().__init__(*_A  ,					**_A      )