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


def 		_UpperCAmelCase (     _lowerCamelCase  :				Dict						)			->  List[str]:
  _lowerCAmelCase				:     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_12, 5_12)						)						)
    elif input_type == "audio":
      inputs.append(torch.ones(30_00						)						)
    elif isinstance(lowercase__     ,					lowercase__						):
      inputs.append(create_inputs(lowercase__						)						)
    else:
      raise ValueError(f'Invalid type requested: {input_type}'						)

  return inputs


def 		_UpperCAmelCase (     _lowerCamelCase  :				str						)			->  int:
  _lowerCAmelCase				:     Any       =    []

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

  return output_types



@is_tool_test
class      a_     :




       def 							__UpperCamelCase				(   self     ):
         self.assertTrue(hasattr(self.tool      ,      """inputs"""     )     )
         self.assertTrue(hasattr(self.tool      ,      """outputs"""     )     )

         _lowerCAmelCase				:     str       =    self.tool.inputs
         for _input in inputs:
           if isinstance(_input      ,      A_     ):
             for __input in _input:
               self.assertTrue(__input in authorized_types     )
           else:
             self.assertTrue(_input in authorized_types     )

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




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Dict       =    create_inputs(self.tool.inputs     )
         _lowerCAmelCase				:     Dict       =    self.tool(*A_     )

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

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




       def 							__UpperCamelCase				(   self     ):
         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 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     List[Any]       =    create_inputs(self.tool.inputs     )
         _lowerCAmelCase				:     Dict       =    self.tool(*A_     )

         if not isinstance(A_      ,      A_     ):
           _lowerCAmelCase				:     List[Any]       =    [outputs]

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

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







       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Any       =    create_inputs(self.tool.inputs     )

         _lowerCAmelCase				:     List[str]       =    []

         for _input, input_type in zip(A_      ,      self.tool.inputs     ):
           if isinstance(A_      ,      A_     ):
             _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
         _lowerCAmelCase				:     Dict       =    self.tool(*A_     )

         if not isinstance(A_      ,      A_     ):
           _lowerCAmelCase				:     Any       =    [outputs]

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





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


_UpperCamelCase 			=		logging.get_logger(__name__)

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







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_="relu"      ,							A_=256      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=1.0      ,							A_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->Any:



       '''simple docstring'''





       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.'''					)
                   __lowerCAmelCase					:						Optional[Any]					=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
             elif isinstance(A_      ,							A_					):
                   __lowerCAmelCase					:						int					=					backbone_config.get('''model_type'''					)
                   __lowerCAmelCase					:						List[str]					=					CONFIG_MAPPING[backbone_model_type]
                   __lowerCAmelCase					:						Any					=					config_class.from_dict(A_					)
             # set timm attributes to None
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					None, None, None

       __lowerCAmelCase					:						Tuple					=					use_timm_backbone
       __lowerCAmelCase					:						Optional[Any]					=					backbone_config
       __lowerCAmelCase					:						List[str]					=					num_channels
       __lowerCAmelCase					:						Tuple					=					num_queries
       __lowerCAmelCase					:						int					=					d_model
       __lowerCAmelCase					:						List[Any]					=					encoder_ffn_dim
       __lowerCAmelCase					:						Optional[int]					=					encoder_layers
       __lowerCAmelCase					:						List[str]					=					encoder_attention_heads
       __lowerCAmelCase					:						str					=					decoder_ffn_dim
       __lowerCAmelCase					:						Union[str, Any]					=					decoder_layers
       __lowerCAmelCase					:						Any					=					decoder_attention_heads
       __lowerCAmelCase					:						Optional[int]					=					dropout
       __lowerCAmelCase					:						Any					=					attention_dropout
       __lowerCAmelCase					:						Tuple					=					activation_dropout
       __lowerCAmelCase					:						Optional[Any]					=					activation_function
       __lowerCAmelCase					:						List[str]					=					init_std
       __lowerCAmelCase					:						Tuple					=					init_xavier_std
       __lowerCAmelCase					:						Any					=					encoder_layerdrop
       __lowerCAmelCase					:						List[Any]					=					decoder_layerdrop
       __lowerCAmelCase					:						Optional[Any]					=					encoder_layers
       __lowerCAmelCase					:						Optional[Any]					=					auxiliary_loss
       __lowerCAmelCase					:						Optional[Any]					=					position_embedding_type
       __lowerCAmelCase					:						Tuple					=					backbone
       __lowerCAmelCase					:						Any					=					use_pretrained_backbone
       __lowerCAmelCase					:						int					=					dilation
       # Hungarian matcher
       __lowerCAmelCase					:						Dict					=					class_cost
       __lowerCAmelCase					:						List[str]					=					bbox_cost
       __lowerCAmelCase					:						int					=					giou_cost
       # Loss coefficients
       __lowerCAmelCase					:						Optional[Any]					=					mask_loss_coefficient
       __lowerCAmelCase					:						Tuple					=					dice_loss_coefficient
       __lowerCAmelCase					:						int					=					bbox_loss_coefficient
       __lowerCAmelCase					:						List[Any]					=					giou_loss_coefficient
       __lowerCAmelCase					:						int					=					eos_coefficient
       super().__init__(is_encoder_decoder=A_      ,							**A_					)
 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





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



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


