



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    _snake_case (       __snake_case    )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		[]

      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(__snake_case					,  __snake_case    ):
                  inputs.append(create_inputs(__snake_case    )    )
            else:
                  raise ValueError(F"""Invalid type requested: {input_type}"""    )

      return inputs







def    _snake_case (       __snake_case    )     ->     List[Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	Union[str, Any]				      =		[]

      for output in outputs:
            if isinstance(__snake_case					,  (str, AgentText)    ):
                  output_types.append("text"    )
            elif isinstance(__snake_case					,  (Image.Image, AgentImage)    ):
                  output_types.append("image"    )
            elif isinstance(__snake_case					,  (torch.Tensor, AgentAudio)    ):
                  output_types.append("audio"    )
            else:
                  raise ValueError(F"""Invalid output: {output}"""    )

      return output_types





@is_tool_test
class   snake_case_ :




       """simple docstring"""






       def  A_   (     self):

             """simple docstring"""





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

             UpperCAmelCase_  :	List[Any]				      =		self.tool.inputs
             for _input in inputs:
                   if isinstance(_input     ,lowercase):
                         for __input in _input:
                               self.assertTrue(__input in authorized_types)
                   else:
                         self.assertTrue(_input in authorized_types)

             UpperCAmelCase_  :	Optional[int]				      =		self.tool.outputs
             for _output in outputs:
                   self.assertTrue(_output in authorized_types)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		create_inputs(self.tool.inputs)
             UpperCAmelCase_  :	Dict				      =		self.tool(*lowercase)

             # There is a single output
             if len(self.tool.outputs) == 1:
                   UpperCAmelCase_  :	Union[str, Any]				      =		[outputs]

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



       def  A_   (     self):

             """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  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		create_inputs(self.tool.inputs)
             UpperCAmelCase_  :	str				      =		self.tool(*lowercase)

             if not isinstance(lowercase     ,lowercase):
                   UpperCAmelCase_  :	List[str]				      =		[outputs]

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

             for output, output_type in zip(lowercase     ,self.tool.outputs):
                   UpperCAmelCase_  :	Dict				      =		AGENT_TYPE_MAPPING[output_type]
                   self.assertTrue(isinstance(lowercase     ,lowercase))



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		create_inputs(self.tool.inputs)

             UpperCAmelCase_  :	List[Any]				      =		[]

             for _input, input_type in zip(lowercase     ,self.tool.inputs):
                   if isinstance(lowercase     ,lowercase):
                         _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
             UpperCAmelCase_  :	Any				      =		self.tool(*lowercase)

             if not isinstance(lowercase     ,lowercase):
                   UpperCAmelCase_  :	Any				      =		[outputs]

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





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


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""lxmert"""
       _lowerCamelCase			    =				{}



       def __init__(     self     ,lowercase=30522     ,lowercase=768     ,lowercase=12     ,lowercase=9500     ,lowercase=1600     ,lowercase=400     ,lowercase=3072     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=2     ,lowercase=0.02     ,lowercase=1E-12     ,lowercase=9     ,lowercase=5     ,lowercase=5     ,lowercase=2048     ,lowercase=4     ,lowercase=6.67     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,**lowercase     ,):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		vocab_size
             UpperCAmelCase_  :	str				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_attention_heads
             UpperCAmelCase_  :	Dict				      =		hidden_act
             UpperCAmelCase_  :	List[Any]				      =		intermediate_size
             UpperCAmelCase_  :	Tuple				      =		hidden_dropout_prob
             UpperCAmelCase_  :	List[Any]				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Any				      =		max_position_embeddings
             UpperCAmelCase_  :	str				      =		type_vocab_size
             UpperCAmelCase_  :	Any				      =		initializer_range
             UpperCAmelCase_  :	Optional[Any]				      =		layer_norm_eps
             UpperCAmelCase_  :	Any				      =		num_qa_labels
             UpperCAmelCase_  :	str				      =		num_object_labels
             UpperCAmelCase_  :	Dict				      =		num_attr_labels
             UpperCAmelCase_  :	Tuple				      =		l_layers
             UpperCAmelCase_  :	Tuple				      =		x_layers
             UpperCAmelCase_  :	int				      =		r_layers
             UpperCAmelCase_  :	Optional[Any]				      =		visual_feat_dim
             UpperCAmelCase_  :	List[Any]				      =		visual_pos_dim
             UpperCAmelCase_  :	int				      =		visual_loss_normalizer
             UpperCAmelCase_  :	str				      =		task_matched
             UpperCAmelCase_  :	str				      =		task_mask_lm
             UpperCAmelCase_  :	int				      =		task_obj_predict
             UpperCAmelCase_  :	List[str]				      =		task_qa
             UpperCAmelCase_  :	Optional[int]				      =		visual_obj_loss
             UpperCAmelCase_  :	List[str]				      =		visual_attr_loss
             UpperCAmelCase_  :	str				      =		visual_feat_loss
             UpperCAmelCase_  :	List[Any]				      =		{"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
             super().__init__(**lowercase)
