





'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


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

a__			:					Any       						=						{
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								="table-transformer"
       _lowerCamelCase								=["past_key_values"]
       _lowerCamelCase								={
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }

       def __init__( self					:				str       ,     a__					:				Any=True       ,     a__					:				str=None       ,     a__					:				Union[str, Any]=3       ,     a__					:				Optional[Any]=100       ,     a__					:				Optional[int]=6       ,     a__					:				List[Any]=2048       ,     a__					:				Optional[Any]=8       ,     a__					:				Dict=6       ,     a__					:				Any=2048       ,     a__					:				List[Any]=8       ,     a__					:				Dict=0.0       ,     a__					:				Dict=0.0       ,     a__					:				List[Any]=True       ,     a__					:				int="relu"       ,     a__					:				str=256       ,     a__					:				Dict=0.1       ,     a__					:				str=0.0       ,     a__					:				List[Any]=0.0       ,     a__					:				Dict=0.02       ,     a__					:				Union[str, Any]=1.0       ,     a__					:				Tuple=False       ,     a__					:				Any="sine"       ,     a__					:				Optional[int]="resnet50"       ,     a__					:				Dict=True       ,     a__					:				Dict=False       ,     a__					:				str=1       ,     a__					:				List[str]=5       ,     a__					:				Union[str, Any]=2       ,     a__					:				Optional[int]=1       ,     a__					:				Dict=1       ,     a__					:				str=5       ,     a__					:				List[Any]=2       ,     a__					:				Optional[Any]=0.1       ,     **a__					:				Optional[int]       ,     ):
              if backbone_config is not None and use_timm_backbone:
                     raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''						)

              if not use_timm_backbone:
                     if backbone_config is None:
                            logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
                            UpperCAmelCase  =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']						)
                     elif isinstance(a__       ,     a__						):
                            UpperCAmelCase  =						backbone_config.get('''model_type'''						)
                            UpperCAmelCase  =						CONFIG_MAPPING[backbone_model_type]
                            UpperCAmelCase  =						config_class.from_dict(a__						)
                     # set timm attributes to None
                     UpperCAmelCase,					UpperCAmelCase,					UpperCAmelCase  =						None, None, None

              UpperCAmelCase  =						use_timm_backbone
              UpperCAmelCase  =						backbone_config
              UpperCAmelCase  =						num_channels
              UpperCAmelCase  =						num_queries
              UpperCAmelCase  =						d_model
              UpperCAmelCase  =						encoder_ffn_dim
              UpperCAmelCase  =						encoder_layers
              UpperCAmelCase  =						encoder_attention_heads
              UpperCAmelCase  =						decoder_ffn_dim
              UpperCAmelCase  =						decoder_layers
              UpperCAmelCase  =						decoder_attention_heads
              UpperCAmelCase  =						dropout
              UpperCAmelCase  =						attention_dropout
              UpperCAmelCase  =						activation_dropout
              UpperCAmelCase  =						activation_function
              UpperCAmelCase  =						init_std
              UpperCAmelCase  =						init_xavier_std
              UpperCAmelCase  =						encoder_layerdrop
              UpperCAmelCase  =						decoder_layerdrop
              UpperCAmelCase  =						encoder_layers
              UpperCAmelCase  =						auxiliary_loss
              UpperCAmelCase  =						position_embedding_type
              UpperCAmelCase  =						backbone
              UpperCAmelCase  =						use_pretrained_backbone
              UpperCAmelCase  =						dilation
              # Hungarian matcher
              UpperCAmelCase  =						class_cost
              UpperCAmelCase  =						bbox_cost
              UpperCAmelCase  =						giou_cost
              # Loss coefficients
              UpperCAmelCase  =						mask_loss_coefficient
              UpperCAmelCase  =						dice_loss_coefficient
              UpperCAmelCase  =						bbox_loss_coefficient
              UpperCAmelCase  =						giou_loss_coefficient
              UpperCAmelCase  =						eos_coefficient
              super().__init__(is_encoder_decoder=a__       ,     **a__						)

       @property
       def 			__snake_case      ( self					:				Dict						):
              return self.encoder_attention_heads



       @property
       def 			__snake_case      ( self					:				Any						):
              return self.d_model






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								=version.parse("1.11"    )

       @property
       def 			__snake_case      ( self					:				Tuple						):
              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                      ('''pixel_mask''', {0: '''batch'''}),
                  ]						)

       @property
       def 			__snake_case      ( self					:				int						):
              return 1e-5



       @property
       def 			__snake_case      ( self					:				Tuple						):
              return 12








'''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       )       )


