


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   a						(				SCREAMING_SNAKE_CASE_   :		int=2				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=True				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False							):






       """simple docstring"""





       UpperCamelCase     :						str 						=		Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_				,      split_batches=SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Dict 						=		torch.nn.Linear(2				,      4							)
       UpperCamelCase     :						List[Any] 						=		torch.optim.AdamW(model.parameters()				,      lr=1.0							)
       UpperCamelCase     :						List[Any] 						=		torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE_				,      max_lr=0.01				,      steps_per_epoch=2				,      epochs=1							)
       UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Optional[int] 						=		accelerator.prepare(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_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   a						(				SCREAMING_SNAKE_CASE_   :		List[str]=2				,      SCREAMING_SNAKE_CASE_   :		List[Any]=True				,      SCREAMING_SNAKE_CASE_   :		List[Any]=False							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE_				,      split_batches=SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Optional[Any] 						=		torch.nn.Linear(2				,      4							)
       UpperCamelCase     :						Dict 						=		torch.optim.AdamW(model.parameters()				,      lr=1.0							)
       UpperCamelCase     :						Any 						=		torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE_				,      lr_lambda=lambda SCREAMING_SNAKE_CASE_							: 1 - n / 1_0							)
       UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Optional[Any] 						=		accelerator.prepare(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)

       # Optimizer has stepped
       UpperCamelCase     :						str 						=		False
       scheduler.step()
       UpperCamelCase     :						Tuple 						=		1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
       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
       UpperCamelCase     :						List[str] 						=		True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              UpperCamelCase     :						Union[str, Any] 						=		1 - 2 / 1_0
       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   a						(				SCREAMING_SNAKE_CASE_   :		int = 2							):






       """simple docstring"""





       from transformers import get_linear_schedule_with_warmup

       UpperCamelCase     :						Optional[int] 						=		[1, 2, 4]
       for num_steps in steps:
              UpperCamelCase     :						Union[str, Any] 						=		GradientAccumulationPlugin(num_steps=SCREAMING_SNAKE_CASE_				,      adjust_scheduler=num_steps > 1							)
              UpperCamelCase     :						Optional[Any] 						=		Accelerator(gradient_accumulation_plugin=SCREAMING_SNAKE_CASE_							)
              UpperCamelCase     :						Tuple 						=		torch.nn.Linear(2				,      4							)
              UpperCamelCase     :						Any 						=		torch.optim.AdamW(model.parameters()				,      lr=10.0							)
              UpperCamelCase     :						Optional[Any] 						=		get_linear_schedule_with_warmup(optimizer=SCREAMING_SNAKE_CASE_				,      num_warmup_steps=0				,      num_training_steps=2_0							)

              UpperCamelCase						,			UpperCamelCase						,			UpperCamelCase     :						Any 						=		accelerator.prepare(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_							)

              for i in range(1_0 * num_steps							):
                     with accelerator.accumulate(SCREAMING_SNAKE_CASE_							):
                            optimizer.step()
                            scheduler.step()

                     if i == (1_0 * 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  UpperCAmelCase_						(				unittest.TestCase):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       def 			_lowercase (  self    ):




              """simple docstring"""




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






       @require_huggingface_suite
       def 			_lowercase (  self    ):




              """simple docstring"""




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





import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


__UpperCAmelCase			:			Optional[Any]			      =					2 << 20  # 2mb
__UpperCAmelCase			:			List[Any]			      =					get_logger(__name__)



class  UpperCAmelCase_						(				_a):
       '''simple docstring'''






       def 			_lowercase (  self    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		self._options.get_all_options().get('''runner'''    )
              return runner in [None, "DirectRunner", "PortableRunner"]




def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False							):






       """simple docstring"""





       UpperCamelCase     :						int 						=		FileSystems
       if fs.exists(SCREAMING_SNAKE_CASE_							):
              if force_upload:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.create(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''rb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		local_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            remote_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						Any 						=		local_file.read(SCREAMING_SNAKE_CASE_							)


def   a						(				SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]				,      SCREAMING_SNAKE_CASE_   :		List[str]=False							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		FileSystems
       if os.path.exists(SCREAMING_SNAKE_CASE_							):
              if force_download:
                     logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""							)
              else:
                     logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""							)
                     return
       with fs.open(SCREAMING_SNAKE_CASE_							) as remote_file:
              with open(SCREAMING_SNAKE_CASE_				,      '''wb'''							) as local_file:
                     UpperCamelCase     :						List[str] 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)
                     while chunk:
                            local_file.write(SCREAMING_SNAKE_CASE_							)
                            UpperCamelCase     :						str 						=		remote_file.read(SCREAMING_SNAKE_CASE_							)

