





"""simple docstring"""



import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 	__lowercase  (     snake_case_						:						Dict=2       ,snake_case_						:						str=True       ,snake_case_						:						Tuple=False		)     ->Any:



    '''simple docstring'''







    __A					:		List[str]						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Dict						=  torch.nn.Linear(2       ,4		)
    __A					:		Tuple						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		int						=  torch.optim.lr_scheduler.OneCycleLR(snake_case_       ,max_lr=0.01       ,steps_per_epoch=2       ,epochs=1		)
    __A				, __A				, __A					:		Optional[int]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 	__lowercase  (     snake_case_						:						str=2       ,snake_case_						:						Dict=True       ,snake_case_						:						Tuple=False		)     ->List[Any]:



    '''simple docstring'''







    __A					:		int						=  Accelerator(step_scheduler_with_optimizer=snake_case_       ,split_batches=snake_case_		)
    __A					:		Any						=  torch.nn.Linear(2       ,4		)
    __A					:		Optional[int]						=  torch.optim.AdamW(model.parameters()       ,lr=1.0		)
    __A					:		Tuple						=  torch.optim.lr_scheduler.LambdaLR(snake_case_       ,lr_lambda=lambda snake_case_		: 1 - n / 10		)
    __A				, __A				, __A					:		Tuple						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

    # Optimizer has stepped
    __A					:		List[Any]						=  False
    scheduler.step()
    __A					:		List[Any]						=  1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

    # Optimizer has not stepped
    __A					:		List[str]						=  True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        __A					:		List[Any]						=  1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 	__lowercase  (     snake_case_						:						int = 2		)     ->str:



    '''simple docstring'''







    from transformers import get_linear_schedule_with_warmup

    __A					:		Any						=  [1, 2, 4]
    for num_steps in steps:
        __A					:		Tuple						=  GradientAccumulationPlugin(num_steps=snake_case_       ,adjust_scheduler=num_steps > 1		)
        __A					:		Optional[Any]						=  Accelerator(gradient_accumulation_plugin=snake_case_		)
        __A					:		Optional[int]						=  torch.nn.Linear(2       ,4		)
        __A					:		Any						=  torch.optim.AdamW(model.parameters()       ,lr=10.0		)
        __A					:		Dict						=  get_linear_schedule_with_warmup(optimizer=snake_case_       ,num_warmup_steps=0       ,num_training_steps=20		)

        __A				, __A				, __A					:		Union[str, Any]						=  accelerator.prepare(snake_case_       ,snake_case_       ,snake_case_		)

        for i in range(10 * num_steps		):
            with accelerator.accumulate(snake_case_		):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
        GradientState._reset_state()






@require_cpu
class 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)      ,		num_processes=1						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		step_scheduler_with_optimizer=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1      ,		split_batches=__lowerCamelCase						)      ,		num_processes=1						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		step_scheduler_with_optimizer=__lowerCamelCase						)						)







 @require_huggingface_suite
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     AcceleratorState._reset_state(__lowerCamelCase						)
     debug_launcher(partial(__lowerCamelCase      ,		num_processes=1						)						)
     debug_launcher(__lowerCamelCase						)











"""simple docstring"""



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

import pytest
import requests

a_				=    pathlib.Path.cwd().joinpath("""project_euler""")
a_				=    pathlib.Path.cwd().joinpath(
    """scripts""", """project_euler_answers.json"""
)

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







def 	__lowercase  (     snake_case_						:						pathlib.Path		)     ->ModuleType:



    '''simple docstring'''







    __A					:		str						=  importlib.util.spec_from_file_location(file_path.name       ,str(snake_case_		)		)
    __A					:		Optional[int]						=  importlib.util.module_from_spec(snake_case_		)  # type: ignore
    spec.loader.exec_module(snake_case_		)  # type: ignore
    return module







def 	__lowercase  (     )     ->list[pathlib.Path]:



    '''simple docstring'''







    __A					:		Optional[int]						=  []
    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(snake_case_		)
    return solution_file_paths







def 	__lowercase  (     )     ->str:



    '''simple docstring'''







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







def 	__lowercase  (     )     ->list[pathlib.Path]:



    '''simple docstring'''







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







def 	__lowercase  (     )     ->list[pathlib.Path]:



    '''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 snake_case_		: F"""{path.parent.name}/{path.name}"""       ,)
def 	__lowercase  (     snake_case_						:						pathlib.Path		)     ->None:



    '''simple docstring'''







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




