





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a_    :				str					=			[(1,)]




def   _SCREAMING_SNAKE_CASE      (	):
     __magic_name__				=       bool(os.getenv('''RUN_SLOW''' ,     0						)						)
     if run_slow:
          assert True
     else:
          assert False, "should have been skipped"




def   _SCREAMING_SNAKE_CASE      (	):
     __magic_name__				=       bool(os.getenv('''RUN_SLOW''' ,     0						)						)
     if run_slow and torch_device == "cuda":
          assert True
     else:
          assert False, "should have been skipped"



@require_torch
class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  @slow
  @require_torch_gpu
  def 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       check_slow_torch_cuda()





  @require_torch_gpu
  @slow
  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       check_slow_torch_cuda()







  @parameterized.expand(A						)
  @slow
  def 			__A      (  self	,  A=None						)		->   str:





       '''simple docstring'''







       check_slow()




@slow
@require_torch_gpu
def   _SCREAMING_SNAKE_CASE      (	):
     check_slow_torch_cuda()




@require_torch_gpu
@slow
def   _SCREAMING_SNAKE_CASE      (	):
     check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param''' ,     [1]						)
def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						Tuple						):
     check_slow()




@pytest.mark.parametrize('''param''' ,     [1]						)
@slow
def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						str						):
     check_slow()






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


a_    :				Union[str, Any]					=			logging.get_logger(__name__)

a_    :				Optional[Any]					=			{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							"""vit-hybrid"""





  def __init__(  self	,  A=None	,  A=7_68	,  A=12	,  A=12	,  A=30_72	,  A="gelu"	,  A=0.0	,  A=0.0	,  A=0.02	,  A=1E-12	,  A=2_24	,  A=1	,  A=3	,  A=[1, 10_24, 24, 24]	,  A=True	,  **A	,  )		->   Union[str, Any]:





       '''simple docstring'''







       super().__init__(**A						)

       if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''						)
            __magic_name__				=       {
                '''global_padding''': '''same''',
                '''layer_type''': '''bottleneck''',
                '''depths''': [3, 4, 9],
                '''out_features''': ['''stage3'''],
                '''embedding_dynamic_padding''': True,
            }

       if isinstance(A	,  A						):
            if "model_type" in backbone_config:
                 __magic_name__				=       CONFIG_MAPPING[backbone_config['''model_type''']]
            else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''						)
                 __magic_name__				=       BitConfig
            __magic_name__				=       backbone_config_class(**A						)

       __magic_name__				=       backbone_featmap_shape
       __magic_name__				=       backbone_config
       __magic_name__				=       hidden_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       num_attention_heads
       __magic_name__				=       intermediate_size
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       layer_norm_eps
       __magic_name__				=       image_size
       __magic_name__				=       patch_size
       __magic_name__				=       num_channels
       __magic_name__				=       qkv_bias







  def 			__A      (  self						)		->   Dict[str, any]:





       '''simple docstring'''







       __magic_name__				=       copy.deepcopy(self.__dict__						)
       __magic_name__				=       self.backbone_config.to_dict()
       __magic_name__				=       self.__class__.model_type
       return output