


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


snake_case    :   str	    =					['''text''', '''image''', '''audio''']
def     __lowerCamelCase						(			UpperCAmelCase_						:		List[str]						):




      """simple docstring"""





      a						: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((512, 512)						)						)
            elif input_type == "audio":
                  inputs.append(torch.ones(3000						)						)
            elif isinstance(UpperCAmelCase_			,  UpperCAmelCase_						):
                  inputs.append(create_inputs(UpperCAmelCase_						)						)
            else:
                  raise ValueError(F'''Invalid type requested: {input_type}'''						)

      return inputs
def     __lowerCamelCase						(			UpperCAmelCase_						:		List						):




      """simple docstring"""





      a						:Optional[Any]										=	[]

      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 		_snake_case  :




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self.assertTrue(hasattr(self.tool ,  '''inputs'''   )   )
       self.assertTrue(hasattr(self.tool ,  '''outputs'''   )   )

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	create_inputs(self.tool.inputs   )
       a						:Dict										=	self.tool(*_lowerCamelCase   )

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

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




 def 	SCREAMING_SNAKE_CASE__    (		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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	create_inputs(self.tool.inputs   )
       a						:Dict										=	self.tool(*_lowerCamelCase   )

       if not isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
             a						:List[str]										=	[outputs]

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

       for output, output_type in zip(_lowerCamelCase ,  self.tool.outputs   ):
             a						:str										=	AGENT_TYPE_MAPPING[output_type]
             self.assertTrue(isinstance(_lowerCamelCase ,  _lowerCamelCase   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	create_inputs(self.tool.inputs   )

       a						:List[Any]										=	[]

       for _input, input_type in zip(_lowerCamelCase ,  self.tool.inputs   ):
             if isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
                   _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
       a						:int										=	self.tool(*_lowerCamelCase   )

       if not isinstance(_lowerCamelCase ,  _lowerCamelCase   ):
             a						:Any										=	[outputs]

       self.assertEqual(len(_lowerCamelCase   ) ,  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


snake_case    :   Optional[Any]	    =					logging.get_logger(__name__)

snake_case    :   Dict	    =					{
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'data2vec-vision'




 def __init__(		self ,  _lowerCamelCase=768 ,  _lowerCamelCase=12 ,  _lowerCamelCase=12 ,  _lowerCamelCase=3072 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=224 ,  _lowerCamelCase=16 ,  _lowerCamelCase=3 ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=False ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=True ,  _lowerCamelCase=[3, 5, 7, 11] ,  _lowerCamelCase=[1, 2, 3, 6] ,  _lowerCamelCase=True ,  _lowerCamelCase=0.4 ,  _lowerCamelCase=256 ,  _lowerCamelCase=1 ,  _lowerCamelCase=False ,  _lowerCamelCase=255 ,  **_lowerCamelCase ,  ):
       super().__init__(**_lowerCamelCase   )

       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Optional[int]										=	num_attention_heads
       a						:Dict										=	intermediate_size
       a						:List[Any]										=	hidden_act
       a						:List[str]										=	hidden_dropout_prob
       a						:Union[str, Any]										=	attention_probs_dropout_prob
       a						:Any										=	initializer_range
       a						:Any										=	layer_norm_eps

       a						:Union[str, Any]										=	image_size
       a						:int										=	patch_size
       a						:Optional[int]										=	num_channels
       a						:Union[str, Any]										=	use_mask_token
       a						:Optional[Any]										=	use_absolute_position_embeddings
       a						:Tuple										=	use_relative_position_bias
       a						:List[Any]										=	use_shared_relative_position_bias
       a						:Dict										=	layer_scale_init_value
       a						:Optional[int]										=	drop_path_rate
       a						:List[str]										=	use_mean_pooling
       # decode head attributes (semantic segmentation)
       a						:str										=	out_indices
       a						:Tuple										=	pool_scales
       # auxiliary head attributes (semantic segmentation)
       a						:List[Any]										=	use_auxiliary_head
       a						:List[Any]										=	auxiliary_loss_weight
       a						:Optional[int]										=	auxiliary_channels
       a						:List[str]										=	auxiliary_num_convs
       a						:str										=	auxiliary_concat_input
       a						:Union[str, Any]										=	semantic_loss_ignore_index







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	version.parse('1.11'							)




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




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return 1e-4





