




'''simple docstring'''







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

import pytest
import requests

__UpperCamelCase			:      Optional[Any]			=					pathlib.Path.cwd().joinpath("""project_euler""")
__UpperCamelCase			:      Optional[int]			=					pathlib.Path.cwd().joinpath(
    """scripts""", """project_euler_answers.json"""
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
      __UpperCamelCase			:      dict[str, str]			=					json.load(file_handle)



def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			List[str]					   = importlib.util.spec_from_file_location(file_path.name    ,      str(__lowercase							)							)
     snake_case :			int					   = importlib.util.module_from_spec(__lowercase							)  # type: ignore
     spec.loader.exec_module(__lowercase							)  # type: ignore
     return module



def 					lowerCamelCase__							(			):
     snake_case :			Optional[Any]					   = []
     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(__lowercase							)
     return solution_file_paths



def 					lowerCamelCase__							(			):
     with open(os.environ["""GITHUB_EVENT_PATH"""]							) as file:
          snake_case :			List[Any]					   = json.load(__lowercase							)
     return event["pull_request"]["url"] + "/files"



def 					lowerCamelCase__							(			):
     snake_case :			List[str]					   = []
     snake_case :			Optional[int]					   = {
         """Accept""": """application/vnd.github.v3+json""",
         """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
     }
     snake_case :			int					   = requests.get(get_files_url()    ,      headers=__lowercase							).json()
     for file in files:
          snake_case :			Dict					   = 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(__lowercase							)
     return solution_file_paths



def 					lowerCamelCase__							(			):
     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 __lowercase							: F'''{path.parent.name}/{path.name}'''    ,      )
def 					lowerCamelCase__							(			__lowercase							):
     snake_case :			Optional[Any]					   = solution_path.parent.name[8:].zfill(3							)
     snake_case :			Union[str, Any]					   = PROBLEM_ANSWERS[problem_number]
     snake_case :			str					   = convert_path_to_module(__lowercase							)
     snake_case :			Union[str, Any]					   = str(solution_module.solution()							)  # type: ignore
     snake_case :			int					   = hashlib.shaaaa(answer.encode()							).hexdigest()
     assert (
         answer == expected
     ), F'''Expected solution to {problem_number} to have hash {expected}, got {answer}'''









'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
      import torch







class 				_a   (a__  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Tuple									=				["""image_processor""", """tokenizer"""]
     lowerCAmelCase_     :    Optional[Any]									=				"""OneFormerImageProcessor"""
     lowerCAmelCase_     :    int									=				("""CLIPTokenizer""", """CLIPTokenizerFast""")







     def __init__(							self       ,__a=None       ,__a=None       ,__a = 77       ,__a = 77       ,**__a       )			->							Optional[Any]:
          if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""       )
          if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""       )

          snake_case :			str					   = max_seq_length
          snake_case :			Any					   = task_seq_length

          super().__init__(__a       ,__a       )







     def  snake_case_			(							self       ,__a=None       ,__a=77       )			->							List[str]:
          if text_list is None:
               raise ValueError("""tokens cannot be None."""       )

          snake_case :			int					   = self.tokenizer(__a       ,padding="""max_length"""       ,max_length=__a       ,truncation=__a       )

          snake_case   ,		snake_case :			int					   = tokens["""attention_mask"""], tokens["""input_ids"""]

          snake_case :			Tuple					   = []
          for attn_mask, input_id in zip(__a       ,__a       ):
               snake_case :			Dict					   = torch.tensor(__a       ) * torch.tensor(__a       )
               token_inputs.append(token.unsqueeze(0       )       )

          snake_case :			Optional[int]					   = torch.cat(__a       ,dim=0       )
          return token_inputs







     def __call__(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							Optional[int]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Union[str, Any]					   = self.image_processor(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Tuple					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Optional[Any]					   = []
               for task in task_inputs:
                    snake_case :			Any					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Optional[int]					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Optional[int]					   = encoded_inputs.text_inputs

               snake_case :			Dict					   = []
               for texts in texts_list:
                    snake_case :			Optional[Any]					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Tuple					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,__a=None       ,__a=None       ,__a=None       ,**__a       )			->							List[str]:

          if task_inputs is None:
               raise ValueError("""You have to specify the task_input. Found None."""       )
          elif images is None:
               raise ValueError("""You have to specify the image. Found None."""       )

          if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs       ):
               raise ValueError("""task_inputs must be semantic, instance, or panoptic."""       )

          snake_case :			Any					   = self.image_processor.encode_inputs(__a       ,__a       ,__a       ,**__a       )

          if isinstance(__a       ,__a       ):
               snake_case :			Optional[int]					   = [task_inputs]

          if isinstance(__a       ,__a       ) and all(isinstance(__a       ,__a       ) for task_input in task_inputs       ):
               snake_case :			Dict					   = []
               for task in task_inputs:
                    snake_case :			str					   = F'''the task is {task}'''
                    task_token_inputs.append(__a       )
               snake_case :			Any					   = self._preprocess_text(__a       ,max_length=self.task_seq_length       )
          else:
               raise TypeError("""Task Inputs should be a string or a list of strings."""       )

          if hasattr(__a       ,"""text_inputs"""       ):
               snake_case :			Dict					   = encoded_inputs.text_inputs

               snake_case :			Tuple					   = []
               for texts in texts_list:
                    snake_case :			Tuple					   = self._preprocess_text(__a       ,max_length=self.max_seq_length       )
                    text_inputs.append(text_input_list.unsqueeze(0       )       )

               snake_case :			Union[str, Any]					   = torch.cat(__a       ,dim=0       )

          return encoded_inputs







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[str]:
          return self.image_processor.post_process_semantic_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							List[Any]:
          return self.image_processor.post_process_instance_segmentation(*__a       ,**__a       )







     def  snake_case_			(							self       ,*__a       ,**__a       )			->							Optional[Any]:
          return self.image_processor.post_process_panoptic_segmentation(*__a       ,**__a       )



