





'''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


snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{
    """microsoft/beit-base-patch16-224-pt22k""": (
        """https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """beit"""


   def __init__( self    ,   _UpperCAmelCase=8_192    ,   _UpperCAmelCase=768    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=12    ,   _UpperCAmelCase=3_072    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=1E-1_2    ,   _UpperCAmelCase=224    ,   _UpperCAmelCase=16    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=[3, 5, 7, 11]    ,   _UpperCAmelCase=[1, 2, 3, 6]    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=0.4    ,   _UpperCAmelCase=256    ,   _UpperCAmelCase=1    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=255    ,   **_UpperCAmelCase    ,   ):
       super().__init__(**_UpperCAmelCase					)

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

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







class   _lowercase						(							a				):
   _UpperCamelCase       		=   version.parse("""1.11"""				)


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


   @property
   def 					snake_case				( self					):
       return 1E-4







'''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


snake_case_     =	["""text""", """image""", """audio"""]
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    A       :       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(3_000		)		)
        elif isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
            inputs.append(create_inputs(UpperCamelCase__		)		)
        else:
            raise ValueError(F'''Invalid type requested: {input_type}'''		)

    return inputs
def 							_lowerCamelCase(  UpperCamelCase__    : List		)		->  Tuple:
    A       :       Optional[int]							=						[]

    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   _lowercase						:


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

       A       :       Any							=						self.tool.inputs
       for _input in inputs:
           if isinstance(_input    ,   _UpperCAmelCase					):
               for __input in _input:
                   self.assertTrue(__input in authorized_types					)
           else:
               self.assertTrue(_input in authorized_types					)

       A       :       Tuple							=						self.tool.outputs
       for _output in outputs:
           self.assertTrue(_output in authorized_types					)


   def 					snake_case				( self					):
       A       :       Any							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

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

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


   def 					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 					snake_case				( self					):
       A       :       List[str]							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       int							=						[outputs]

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

       for output, output_type in zip(_UpperCAmelCase    ,   self.tool.outputs					):
           A       :       Tuple							=						AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)					)


   def 					snake_case				( self					):
       A       :       Tuple							=						create_inputs(self.tool.inputs					)

       A       :       Dict							=						[]

       for _input, input_type in zip(_UpperCAmelCase    ,   self.tool.inputs					):
           if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               _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       :       List[Any]							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Any							=						[outputs]

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