






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



def   a    (     __UpperCAmelCase      :    List[str]  )    ->							Optional[Any]:
						__magic_name__:	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_1_2, 5_1_2)  )  )
												elif input_type == "audio":
																		inputs.append(torch.ones(3_0_0_0  )  )
												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:
						__magic_name__:	Union[str, Any]							=       []

						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       __A		:

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Union[str, Any]:
											self.assertTrue(hasattr(self.tool     ,		"""inputs"""					)					)
											self.assertTrue(hasattr(self.tool     ,		"""outputs"""					)					)

											__magic_name__:	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					)

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

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							int:
											__magic_name__:	Union[str, Any]							=       create_inputs(self.tool.inputs					)
											__magic_name__:	Dict							=       self.tool(*_UpperCAmelCase					)

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

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

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

											if not isinstance(_UpperCAmelCase     ,		_UpperCAmelCase					):
																	__magic_name__:	int							=       [outputs]

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

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






					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Union[str, Any]:
											__magic_name__:	Dict							=       create_inputs(self.tool.inputs					)

											__magic_name__:	Union[str, 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
											__magic_name__:	List[str]							=       self.tool(*_UpperCAmelCase					)

											if not isinstance(_UpperCAmelCase     ,		_UpperCAmelCase					):
																	__magic_name__:	Optional[int]							=       [outputs]

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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			:						List[str]        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''ibert'''

					def __init__(self       :		int ,   _UpperCAmelCase       :		Union[str, Any]=3_0522 ,   _UpperCAmelCase       :		Any=768 ,   _UpperCAmelCase       :		Optional[Any]=12 ,   _UpperCAmelCase       :		Union[str, Any]=12 ,   _UpperCAmelCase       :		List[Any]=3072 ,   _UpperCAmelCase       :		List[Any]="gelu" ,   _UpperCAmelCase       :		List[str]=0.1 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[int]=512 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Dict=1E-1_2 ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		str=0 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		int="absolute" ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]="none" ,   **_UpperCAmelCase       :		List[Any] ,   )		->  Any:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase ,   bos_token_id=_UpperCAmelCase ,   eos_token_id=_UpperCAmelCase ,   **_UpperCAmelCase						)

							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps
							lowercase__	     =						position_embedding_type
							lowercase__	     =						quant_mode
							lowercase__	     =						force_dequant






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__	     =						{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__	     =						{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]						)

