"""simple docstring"""




import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types
class      UpperCamelCase__(      unittest.TestCase		,    __A			):





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				load_tool('translation'  )
													self.tool.setup()
													A__						  =				load_tool('translation'		,remote=__UpperCAmelCase  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				self.tool('Hey, what\'s up?'		,src_lang='English'		,tgt_lang='French'  )
													self.assertEqual(__UpperCAmelCase		,'- Hé, comment ça va?'  )





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				self.remote_tool('Hey, what\'s up?'		,src_lang='English'		,tgt_lang='French'  )
													self.assertEqual(__UpperCAmelCase		,'- Hé, comment ça va?'  )





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				self.tool(text='Hey, what\'s up?'		,src_lang='English'		,tgt_lang='French'  )
													self.assertEqual(__UpperCAmelCase		,'- Hé, comment ça va?'  )





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				self.remote_tool(text='Hey, what\'s up?'		,src_lang='English'		,tgt_lang='French'  )
													self.assertEqual(__UpperCAmelCase		,'- Hé, comment ça va?'  )





						def 					snake_case__						(							self  )       ->					Tuple:
													A__						  =				['Hey, what\'s up?', 'English', 'Spanish']
													A__						  =				self.tool(*__UpperCAmelCase  )

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

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





						def 					snake_case__						(							self  )       ->					Dict:
													A__						  =				['Hey, what\'s up?', 'English', 'Spanish']
													A__						  =				self.tool(*__UpperCAmelCase  )

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

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

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





						def 					snake_case__						(							self  )       ->					List[str]:
													A__						  =				['Hey, what\'s up?', 'English', 'Spanish']

													A__						  =				[]

													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
													A__						  =				self.tool(*__UpperCAmelCase  )

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

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






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase									=	logging.get_logger(__name__)

__lowerCamelCase									=	{
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    str    				=		'vilt'





						def __init__(							self		,__UpperCAmelCase=3_05_22		,__UpperCAmelCase=2		,__UpperCAmelCase=2		,__UpperCAmelCase=40		,__UpperCAmelCase=7_68		,__UpperCAmelCase=12		,__UpperCAmelCase=12		,__UpperCAmelCase=30_72		,__UpperCAmelCase="gelu"		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0		,__UpperCAmelCase=0.0_2		,__UpperCAmelCase=1e-12		,__UpperCAmelCase=3_84		,__UpperCAmelCase=32		,__UpperCAmelCase=3		,__UpperCAmelCase=True		,__UpperCAmelCase=-1		,__UpperCAmelCase=False		,__UpperCAmelCase=-1		,**__UpperCAmelCase		,)       ->					Tuple:
													super().__init__(tie_word_embeddings=__UpperCAmelCase		,**__UpperCAmelCase  )

													A__						  =				vocab_size
													A__						  =				type_vocab_size
													A__						  =				modality_type_vocab_size
													A__						  =				max_position_embeddings

													A__						  =				hidden_size
													A__						  =				num_hidden_layers
													A__						  =				num_attention_heads
													A__						  =				intermediate_size
													A__						  =				hidden_act
													A__						  =				hidden_dropout_prob
													A__						  =				attention_probs_dropout_prob
													A__						  =				initializer_range
													A__						  =				layer_norm_eps

													A__						  =				image_size
													A__						  =				patch_size
													A__						  =				num_channels
													A__						  =				qkv_bias
													A__						  =				max_image_length
													A__						  =				num_images





