





'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


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

a__							:							str ={
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					="dpt"






 def __init__(       self       :							int							,	__A       :							Optional[int]=7_6_8							,	__A       :							Any=1_2							,	__A       :							Tuple=1_2							,	__A       :							Optional[Any]=3_0_7_2							,	__A       :							List[Any]="gelu"							,	__A       :							Tuple=0.0							,	__A       :							List[Any]=0.0							,	__A       :							List[Any]=0.02							,	__A       :							Dict=1e-12							,	__A       :							List[Any]=3_8_4							,	__A       :							List[str]=1_6							,	__A       :							Union[str, Any]=3							,	__A       :							Optional[int]=False							,	__A       :							str=True							,	__A       :							Union[str, Any]=[2, 5, 8, 1_1]							,	__A       :							Any="project"							,	__A       :							List[str]=[4, 2, 1, 0.5]							,	__A       :							Union[str, Any]=[9_6, 1_9_2, 3_8_4, 7_6_8]							,	__A       :							List[str]=2_5_6							,	__A       :							str=-1							,	__A       :							Dict=False							,	__A       :							Tuple=True							,	__A       :							Union[str, Any]=0.4							,	__A       :							int=2_5_5							,	__A       :							Tuple=0.1							,	__A       :							Optional[Any]=[1, 1_0_2_4, 2_4, 2_4]							,	__A       :							List[str]=[0, 1]							,	__A       :							Optional[Any]=None							,	**__A       :							Tuple							,	):
        super().__init__(**__A	)

        __UpperCamelCase       =  hidden_size
        __UpperCamelCase       =  is_hybrid

        if self.is_hybrid:
               if backbone_config is None:
                      logger.info('Initializing the config with a `BiT` backbone.'	)
                      __UpperCamelCase       =  {
                          'global_padding': 'same',
                          'layer_type': 'bottleneck',
                          'depths': [3, 4, 9],
                          'out_features': ['stage1', 'stage2', 'stage3'],
                          'embedding_dynamic_padding': True,
                      }
                      __UpperCamelCase       =  BitConfig(**__A	)
               elif isinstance(__A							,	__A	):
                      logger.info('Initializing the config with a `BiT` backbone.'	)
                      __UpperCamelCase       =  BitConfig(**__A	)
               elif isinstance(__A							,	__A	):
                      __UpperCamelCase       =  backbone_config
               else:
                      raise ValueError(
                          f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''	)

               __UpperCamelCase       =  backbone_featmap_shape
               __UpperCamelCase       =  neck_ignore_stages

               if readout_type != "project":
                      raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'	)
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  []

        __UpperCamelCase       =  num_hidden_layers
        __UpperCamelCase       =  num_attention_heads
        __UpperCamelCase       =  intermediate_size
        __UpperCamelCase       =  hidden_act
        __UpperCamelCase       =  hidden_dropout_prob
        __UpperCamelCase       =  attention_probs_dropout_prob
        __UpperCamelCase       =  initializer_range
        __UpperCamelCase       =  layer_norm_eps
        __UpperCamelCase       =  image_size
        __UpperCamelCase       =  patch_size
        __UpperCamelCase       =  num_channels
        __UpperCamelCase       =  qkv_bias
        __UpperCamelCase       =  backbone_out_indices
        if readout_type not in ["ignore", "add", "project"]:
               raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'	)
        __UpperCamelCase       =  readout_type
        __UpperCamelCase       =  reassemble_factors
        __UpperCamelCase       =  neck_hidden_sizes
        __UpperCamelCase       =  fusion_hidden_size
        __UpperCamelCase       =  head_in_index
        __UpperCamelCase       =  use_batch_norm_in_fusion_residual
        # auxiliary head attributes (semantic segmentation)
        __UpperCamelCase       =  use_auxiliary_head
        __UpperCamelCase       =  auxiliary_loss_weight
        __UpperCamelCase       =  semantic_loss_ignore_index
        __UpperCamelCase       =  semantic_classifier_dropout






 def        _lowerCamelCase					(       self       :							List[Any]	):
        __UpperCamelCase       =  copy.deepcopy(self.__dict__	)

        if output["backbone_config"] is not None:
               __UpperCamelCase       =  self.backbone_config.to_dict()

        __UpperCamelCase       =  self.__class__.model_type
        return output


'''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


_lowercase :  List[str]    							=       ["text", "image", "audio"]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     int													=       []

						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(3000							)							)
												elif isinstance(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																		inputs.append(create_inputs(__SCREAMING_SNAKE_CASE							)							)
												else:
																		raise ValueError(F'''Invalid type requested: {input_type}'''							)

						return inputs
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List							):



						"""simple docstring"""





						lowercase_	:     Optional[Any]													=       []

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

						return output_types






@is_tool_test
class  lowerCAmelCase__   :






					def 	_snake_case    (   self		):


											"""simple docstring"""





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

											lowercase_	:     Optional[Any]													=       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		)

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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     Tuple													=       self.tool(*__SCREAMING_SNAKE_CASE		)

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

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






					def 	_snake_case    (   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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     int													=       create_inputs(self.tool.inputs		)
											lowercase_	:     int													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Optional[Any]													=       [outputs]

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

											for output, output_type in zip(__SCREAMING_SNAKE_CASE				,							self.tool.outputs		):
																	lowercase_	:     Optional[int]													=       AGENT_TYPE_MAPPING[output_type]
																	self.assertTrue(isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       create_inputs(self.tool.inputs		)

											lowercase_	:     int													=       []

											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
											lowercase_	:     Optional[Any]													=       self.tool(*__SCREAMING_SNAKE_CASE		)

											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Dict													=       [outputs]

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



