




from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
						import torch

__a  :		Any		     =   logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
						from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):
						def  snake_case_					(		cls    )      ->			str:
													return cls
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_ = None    ,SCREAMING_SNAKE_CASE_ = None    ,SCREAMING_SNAKE_CASE_ = None    ,SCREAMING_SNAKE_CASE_ = None    ,)      ->			Optional[int]:
							lowercase__  :	Union[str, Any]        =							device
							lowercase__  :	int        =							shape[0]

							lowercase__  :	Tuple        =							layout or torch.strided
							lowercase__  :	Any        =							device or torch.device("cpu"    )

							if generator is not None:
														lowercase__  :	Dict        =							generator.device.type if not isinstance(_lowerCAmelCase    ,_lowerCAmelCase    ) else generator[0].device.type
														if gen_device_type != device.type and gen_device_type == "cpu":
																					lowercase__  :	Union[str, Any]        =							"cpu"
																					if device != "mps":
																												logger.info(
																												    F"""The passed generator was created on 'cpu' even though a tensor on {device} was expected."""
																												    F""" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"""
																												    F""" slighly speed up this function by passing a generator that was created on the {device} device."""    )
														elif gen_device_type != device.type and gen_device_type == "cuda":
																					raise ValueError(F"""Cannot generate a {device} tensor from a generator of type {gen_device_type}."""    )

							if isinstance(_lowerCAmelCase    ,_lowerCAmelCase    ):
														lowercase__  :	Optional[Any]        =							(1,) + shape[1:]
														lowercase__  :	int        =							[
														    torch.randn(_lowerCAmelCase    ,generator=generator[i]    ,device=_lowerCAmelCase    ,dtype=_lowerCAmelCase    ,layout=_lowerCAmelCase    )
														    for i in range(_lowerCAmelCase    )
														]
														lowercase__  :	int        =							torch.cat(_lowerCAmelCase    ,dim=0    ).to(_lowerCAmelCase    )
							else:
														lowercase__  :	Optional[int]        =							torch.randn(_lowerCAmelCase    ,generator=_lowerCAmelCase    ,device=_lowerCAmelCase    ,dtype=_lowerCAmelCase    ,layout=_lowerCAmelCase    ).to(_lowerCAmelCase    )

							return latents




def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			List[Any]:
							if is_torch_version("<"    ,"2.0.0"    ) or not hasattr(_lowerCAmelCase    ,"_dynamo"    ):
														return False
							return isinstance(_lowerCAmelCase    ,torch._dynamo.eval_frame.OptimizedModule    )





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

import pytest
import requests

__a  :		str		     =   pathlib.Path.cwd().joinpath('''project_euler''')
__a  :		Tuple		     =   pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
						__a  :		dict[str, str]		     =   json.load(file_handle)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			ModuleType:
							lowercase__  :	List[str]        =							importlib.util.spec_from_file_location(file_path.name    ,str(SCREAMING_SNAKE_CASE_    )    )
							lowercase__  :	int        =							importlib.util.module_from_spec(SCREAMING_SNAKE_CASE_    )  # type: ignore
							spec.loader.exec_module(SCREAMING_SNAKE_CASE_    )  # type: ignore
							return module
def  snake_case_					(		)      ->			list[pathlib.Path]:
							lowercase__  :	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  snake_case_					(		)      ->			str:
							with open(os.environ["GITHUB_EVENT_PATH"]    ) as file:
														lowercase__  :	str        =							json.load(SCREAMING_SNAKE_CASE_    )
							return event["pull_request"]["url"] + "/files"
def  snake_case_					(		)      ->			list[pathlib.Path]:
							lowercase__  :	Optional[Any]        =							[]
							lowercase__  :	Dict        =							{
							    "Accept": "application/vnd.github.v3+json",
							    "Authorization": "token " + os.environ["GITHUB_TOKEN"],
							}
							lowercase__  :	Union[str, Any]        =							requests.get(get_files_url()    ,headers=SCREAMING_SNAKE_CASE_    ).json()
							for file in files:
														lowercase__  :	Optional[Any]        =							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  snake_case_					(		)      ->			list[pathlib.Path]:
							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  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			None:
							lowercase__  :	str        =							solution_path.parent.name[8:].zfill(3    )
							lowercase__  :	str        =							PROBLEM_ANSWERS[problem_number]
							lowercase__  :	Optional[Any]        =							convert_path_to_module(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	str        =							str(solution_module.solution()    )  # type: ignore
							lowercase__  :	List[str]        =							hashlib.shaaaa(answer.encode()    ).hexdigest()
							assert (
							    answer == expected
							), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""