






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''uclanlp/visualbert-vqa''': '''https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-pre''': '''https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vqa-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-vcr''': '''https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-pre''': '''https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-vcr-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'''
    ),
    '''uclanlp/visualbert-nlvr2''': '''https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-pre''': '''https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json''',
    '''uclanlp/visualbert-nlvr2-coco-pre''': (
        '''https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'''
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''visual_bert'''





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[Any]=30_522      ,      lowerCAmelCase_   :					str=768      ,      lowerCAmelCase_   :					List[str]=512      ,      lowerCAmelCase_   :					int=12      ,      lowerCAmelCase_   :					List[str]=12      ,      lowerCAmelCase_   :					List[str]=3_072      ,      lowerCAmelCase_   :					Any="gelu"      ,      lowerCAmelCase_   :					Tuple=0.1      ,      lowerCAmelCase_   :					Tuple=0.1      ,      lowerCAmelCase_   :					List[str]=512      ,      lowerCAmelCase_   :					Dict=2      ,      lowerCAmelCase_   :					Optional[int]=0.0_2      ,      lowerCAmelCase_   :					Any=1e-12      ,      lowerCAmelCase_   :					Dict=False      ,      lowerCAmelCase_   :					Union[str, Any]=True      ,      lowerCAmelCase_   :					List[str]=1      ,      lowerCAmelCase_   :					Optional[Any]=0      ,      lowerCAmelCase_   :					Optional[Any]=2      ,      **lowerCAmelCase_   :					int      ,      )		->     Optional[int]:
				super().__init__(pad_token_id=lowerCAmelCase_      ,      bos_token_id=lowerCAmelCase_      ,      eos_token_id=lowerCAmelCase_      ,      **lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   vocab_size
				UpperCAmelCase_	:					Optional[Any]     	=   max_position_embeddings
				UpperCAmelCase_	:					Union[str, Any]     	=   hidden_size
				UpperCAmelCase_	:					Dict     	=   visual_embedding_dim
				UpperCAmelCase_	:					Optional[int]     	=   num_hidden_layers
				UpperCAmelCase_	:					Dict     	=   num_attention_heads
				UpperCAmelCase_	:					Tuple     	=   intermediate_size
				UpperCAmelCase_	:					Optional[Any]     	=   hidden_act
				UpperCAmelCase_	:					List[str]     	=   hidden_dropout_prob
				UpperCAmelCase_	:					Any     	=   attention_probs_dropout_prob
				UpperCAmelCase_	:					int     	=   initializer_range
				UpperCAmelCase_	:					str     	=   type_vocab_size
				UpperCAmelCase_	:					List[Any]     	=   layer_norm_eps
				UpperCAmelCase_	:					Optional[Any]     	=   bypass_transformer
				UpperCAmelCase_	:					int     	=   special_visual_initialize









'''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


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





def       __a      (  _UpperCamelCase:       List[str]       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     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       )		->					Dict:

     """simple docstring"""




     _snake_case				= []

     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 							_lowercase      (					self       )				->     Any:
          self.assertTrue(hasattr(self.tool   ,"inputs"       )       )
          self.assertTrue(hasattr(self.tool   ,"outputs"       )       )

          _snake_case				= self.tool.inputs
          for _input in inputs:
               if isinstance(_input   ,_SCREAMING_SNAKE_CASE       ):
                    for __input in _input:
                         self.assertTrue(__input in authorized_types       )
               else:
                    self.assertTrue(_input in authorized_types       )

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






     def 							_lowercase      (					self       )				->     Any:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

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

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






     def 							_lowercase      (					self       )				->     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 							_lowercase      (					self       )				->     Tuple:
          _snake_case				= create_inputs(self.tool.inputs       )
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

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

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






     def 							_lowercase      (					self       )				->     Optional[Any]:
          _snake_case				= create_inputs(self.tool.inputs       )

          _snake_case				= []

          for _input, input_type in zip(_SCREAMING_SNAKE_CASE   ,self.tool.inputs       ):
               if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                    _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
          _snake_case				= self.tool(*_SCREAMING_SNAKE_CASE       )

          if not isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
               _snake_case				= [outputs]

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


