
'''simple docstring'''






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

import pytest
import requests

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

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







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       Any    							=importlib.util.spec_from_file_location(file_path.name  ,					str(_SCREAMING_SNAKE_CASE  )  )
		lowerCAmelCase_   :       Optional[int]    							=importlib.util.module_from_spec(_SCREAMING_SNAKE_CASE  )  # type: ignore
		spec.loader.exec_module(_SCREAMING_SNAKE_CASE  )  # type: ignore
		return module







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       List[str]    							=[]
		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(_SCREAMING_SNAKE_CASE  )
		return solution_file_paths







def 		SCREAMING_SNAKE_CASE__					(							):
		with open(os.environ['''GITHUB_EVENT_PATH''']  ) as file:
				lowerCAmelCase_   :       Union[str, Any]    							=json.load(_SCREAMING_SNAKE_CASE  )
		return event["pull_request"]["url"] + "/files"







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       List[Any]    							=[]
		lowerCAmelCase_   :       List[Any]    							={
		    '''Accept''': '''application/vnd.github.v3+json''',
		    '''Authorization''': '''token ''' + os.environ['''GITHUB_TOKEN'''],
		}
		lowerCAmelCase_   :       int    							=requests.get(get_files_url()  ,					headers=_SCREAMING_SNAKE_CASE  ).json()
		for file in files:
				lowerCAmelCase_   :       Optional[int]    							=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(_SCREAMING_SNAKE_CASE  )
		return solution_file_paths







def 		SCREAMING_SNAKE_CASE__					(							):
		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 _SCREAMING_SNAKE_CASE  : f'{path.parent.name}/{path.name}'  ,					)
def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       str    							=solution_path.parent.name[8:].zfill(3  )
		lowerCAmelCase_   :       str    							=PROBLEM_ANSWERS[problem_number]
		lowerCAmelCase_   :       Optional[Any]    							=convert_path_to_module(_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Tuple    							=str(solution_module.solution()  )  # type: ignore
		lowerCAmelCase_   :       Dict    							=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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase	=    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






