




'''simple docstring'''







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


lowerCAmelCase_    	=							logging.get_logger(__name__)

lowerCAmelCase_    	=							{
    '''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   _snake_case( UpperCAmelCase			):
 __snake_case:       Any									=				'''vit-hybrid'''






 def __init__(self					:     List[str]						,		a					:     List[str]=None						,		a					:     Any=7_68						,		a					:     List[Any]=12						,		a					:     str=12						,		a					:     Tuple=30_72						,		a					:     Tuple="gelu"						,		a					:     List[str]=0.0						,		a					:     List[str]=0.0						,		a					:     Any=0.02						,		a					:     Optional[Any]=1e-12						,		a					:     Any=2_24						,		a					:     Union[str, Any]=1						,		a					:     Optional[int]=3						,		a					:     Dict=[1, 10_24, 24, 24]						,		a					:     int=True						,		**a					:     List[Any]						,		)					->      Optional[Any]:






    """simple docstring"""


    super().__init__(**a						)

    if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'						)
       A__         =    {
           '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:
          A__         =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
          logger.info(
              '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'						)
          A__         =    BitConfig
       A__         =    backbone_config_class(**a						)

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






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






    """simple docstring"""


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








'''simple docstring'''







import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath('''project_euler''')
lowerCAmelCase_    	=							pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
    lowerCAmelCase_    	=							json.load(file_handle)







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    importlib.util.spec_from_file_location(file_path.name			,str(UpperCAmelCase	)	)
   A__         =    importlib.util.module_from_spec(UpperCAmelCase	)  # type: ignore
   spec.loader.exec_module(UpperCAmelCase	)  # type: ignore
   return module







def 	_A			(   ):






   '''simple docstring'''







   A__         =    []
   for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
      if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'	):
         continue
      for file_path in problem_dir_path.iterdir():
         if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')	):
            continue
         solution_file_paths.append(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''simple docstring'''







   with open(os.environ['GITHUB_EVENT_PATH']	) as file:
      A__         =    json.load(UpperCAmelCase	)
   return event["pull_request"]["url"] + "/files"







def 	_A			(   ):






   '''simple docstring'''







   A__         =    []
   A__         =    {
       'Accept': 'application/vnd.github.v3+json',
       'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
   }
   A__         =    requests.get(get_files_url()			,headers=UpperCAmelCase	).json()
   for file in files:
      A__         =    pathlib.Path.cwd().joinpath(file['filename']	)
      if (
          filepath.suffix != ".py"
          or filepath.name.startswith(('_', 'test')	)
          or not filepath.name.startswith('sol'	)
      ):
         continue
      solution_file_paths.append(UpperCAmelCase	)
   return solution_file_paths







def 	_A			(   ):






   '''simple docstring'''







   if os.environ.get('CI'	) and os.environ.get('GITHUB_EVENT_NAME'	) == "pull_request":
      # Return only if there are any, otherwise default to all solutions
      if filepaths := added_solution_file_path():
         return filepaths
   return all_solution_file_paths()







@pytest.mark.parametrize(
    'solution_path'			,collect_solution_file_paths()			,ids=lambda UpperCAmelCase	: F"""{path.parent.name}/{path.name}"""			,)
def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    solution_path.parent.name[8:].zfill(3	)
   A__         =    PROBLEM_ANSWERS[problem_number]
   A__         =    convert_path_to_module(UpperCAmelCase	)
   A__         =    str(solution_module.solution()	)  # type: ignore
   A__         =    hashlib.shaaaa(answer.encode()	).hexdigest()
   assert (
       answer == expected
   ), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""


