code: 
  
  
  
  
  
  import hashlib
  import importlib.util
  import json
  import os
  import pathlib
  from types import ModuleType
  
  import pytest
  import requests
  
  __lowerCAmelCase							:			Optional[Any]				=pathlib.Path.cwd().joinpath("project_euler")
  __lowerCAmelCase							:			Optional[int]				=pathlib.Path.cwd().joinpath(
      "scripts", "project_euler_answers.json"
  )
  
  with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
    __lowerCAmelCase							:			dict[str, str]				=json.load(file_handle)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Dict ):
      A__       =    importlib.util.spec_from_file_location(file_path.name		, str(_lowerCamelCase ) )
      A__       =    importlib.util.module_from_spec(_lowerCamelCase )  # type: ignore
      spec.loader.exec_module(_lowerCamelCase )  # type: ignore
      return module
  
  
  
  
  
  def 		UpperCamelCase       (							):
      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(_lowerCamelCase )
      return solution_file_paths
  
  
  
  
  
  def 		UpperCamelCase       (							):
      with open(os.environ["GITHUB_EVENT_PATH"] ) as file:
          A__       =    json.load(_lowerCamelCase )
      return event["pull_request"]["url"] + "/files"
  
  
  
  
  
  def 		UpperCamelCase       (							):
      A__       =    []
      A__       =    {
          "Accept": "application/vnd.github.v3+json",
          "Authorization": "token " + os.environ["GITHUB_TOKEN"],
      }
      A__       =    requests.get(get_files_url()		, headers=_lowerCamelCase ).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(_lowerCamelCase )
      return solution_file_paths
  
  
  
  
  
  def 		UpperCamelCase       (							):
      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 _lowerCamelCase : F"{path.parent.name}/{path.name}"		, )
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any] ):
      A__       =    solution_path.parent.name[8:].zfill(3 )
      A__       =    PROBLEM_ANSWERS[problem_number]
      A__       =    convert_path_to_module(_lowerCamelCase )
      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}"
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  