





"""simple docstring"""



import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


a_				=    get_logger(__name__)


if TYPE_CHECKING:
							from .builder import DatasetBuilder







def 	__lowercase  (     snake_case_						:						Union[str, Any]       ,snake_case_						:						Optional[Union[str, bool]] = None		)     ->Dict:



				'''simple docstring'''







				__A					:		List[str]						=  importlib.import_module(snake_case_		)

				# TODO(QL): always update the module to add subsequent new authentication
				if hasattr(snake_case_       ,'''_patched_for_streaming'''		) and module._patched_for_streaming:
								return

				def wrap_auth(snake_case_						:						Optional[int]		):
								@wraps(snake_case_		)
								def wrapper(*snake_case_						:						int       ,**snake_case_						:						List[Any]		):
												return function(*snake_case_       ,use_auth_token=snake_case_       ,**snake_case_		)

								__A					:		Any						=  '''wrap_auth'''
								return wrapper

				# open files in a streaming fashion
				patch_submodule(snake_case_       ,'''open'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''os.listdir'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''os.walk'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''glob.glob'''       ,wrap_auth(snake_case_		)		).start()
				# allow to navigate in remote zip files
				patch_submodule(snake_case_       ,'''os.path.join'''       ,snake_case_		).start()
				patch_submodule(snake_case_       ,'''os.path.dirname'''       ,snake_case_		).start()
				patch_submodule(snake_case_       ,'''os.path.basename'''       ,snake_case_		).start()
				patch_submodule(snake_case_       ,'''os.path.relpath'''       ,snake_case_		).start()
				patch_submodule(snake_case_       ,'''os.path.split'''       ,snake_case_		).start()
				patch_submodule(snake_case_       ,'''os.path.splitext'''       ,snake_case_		).start()
				# allow checks on paths
				patch_submodule(snake_case_       ,'''os.path.exists'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''os.path.isdir'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''os.path.isfile'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''os.path.getsize'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''pathlib.Path'''       ,snake_case_		).start()
				# file readers
				patch_submodule(snake_case_       ,'''gzip.open'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''numpy.load'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''pandas.read_csv'''       ,wrap_auth(snake_case_		)       ,attrs=['''__version__''']		).start()
				patch_submodule(snake_case_       ,'''pandas.read_excel'''       ,wrap_auth(snake_case_		)       ,attrs=['''__version__''']		).start()
				patch_submodule(snake_case_       ,'''scipy.io.loadmat'''       ,wrap_auth(snake_case_		)       ,attrs=['''__version__''']		).start()
				patch_submodule(snake_case_       ,'''xml.etree.ElementTree.parse'''       ,wrap_auth(snake_case_		)		).start()
				patch_submodule(snake_case_       ,'''xml.dom.minidom.parse'''       ,wrap_auth(snake_case_		)		).start()
				__A					:		Optional[Any]						=  True







def 	__lowercase  (     snake_case_						:						"DatasetBuilder"		)     ->int:



				'''simple docstring'''







				extend_module_for_streaming(builder.__module__       ,use_auth_token=builder.use_auth_token		)
				# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
				if not builder.__module__.startswith('''datasets.'''		):  # check that it's not a packaged builder like csv
								for imports in get_imports(inspect.getfile(builder.__class__		)		):
												if imports[0] == "internal":
																__A					:		List[Any]						=  imports[1]
																__A					:		Dict						=  '''.'''.join(builder.__module__.split('''.'''		)[:-1] + [internal_import_name]		)
																extend_module_for_streaming(snake_case_       ,use_auth_token=builder.use_auth_token		)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
				from .builder import DatasetBuilder

				__A					:		Any						=  [
				    cls.__module__
				    for cls in type(snake_case_		).__mro__[1:]  # make sure it's not the same module we've already patched
				    if issubclass(cls       ,snake_case_		) and cls.__module__ != DatasetBuilder.__module__
				]  # check it's not a standard builder from datasets.builder
				for module in parent_builder_modules:
								extend_module_for_streaming(snake_case_       ,use_auth_token=builder.use_auth_token		)











"""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						)




