
"""simple docstring"""




import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
   import transformers


def   __magic_name__	(      __snake_case				:       int  ) ->				Tuple:
       random.seed(__a  )
       np.random.seed(__a  )
       torch.manual_seed(__a  )
       torch.cuda.manual_seed_all(__a  )
       # ^^ safe to call this function even if cuda is not available

class 		a__						:
   def __init__(			self	,    _a	,    _a = 0.9_9_9_9	,    _a = 0.0	,    _a = 0	,    _a = False	,    _a = 1.0	,    _a = 2 / 3	,    _a = None	,    _a = None	,    **_a	,    ):






          if isinstance(_a	,    torch.nn.Module				):
                 lowercase    :				str       		=							(
                     'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
                     'Please pass the parameters of the module instead.'
                 )
                 deprecate(
                     "passing a `torch.nn.Module` to `ExponentialMovingAverage`"	,    "1.0.0"	,    _a	,    standard_warn=_a	,    )
                 lowercase    :				Optional[int]       		=							parameters.parameters()

                 # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
                 lowercase    :				List[str]       		=							True

          if kwargs.get("max_value"	,    _a				) is not None:
                 lowercase    :				Optional[int]       		=							'The `max_value` argument is deprecated. Please use `decay` instead.'
                 deprecate("max_value"	,    "1.0.0"	,    _a	,    standard_warn=_a				)
                 lowercase    :				Union[str, Any]       		=							kwargs['max_value']

          if kwargs.get("min_value"	,    _a				) is not None:
                 lowercase    :				List[str]       		=							'The `min_value` argument is deprecated. Please use `min_decay` instead.'
                 deprecate("min_value"	,    "1.0.0"	,    _a	,    standard_warn=_a				)
                 lowercase    :				Optional[int]       		=							kwargs['min_value']

          lowercase    :				Tuple       		=							list(_a				)
          lowercase    :				List[str]       		=							[p.clone().detach() for p in parameters]

          if kwargs.get("device"	,    _a				) is not None:
                 lowercase    :				str       		=							'The `device` argument is deprecated. Please use `to` instead.'
                 deprecate("device"	,    "1.0.0"	,    _a	,    standard_warn=_a				)
                 self.to(device=kwargs["device"]				)

          lowercase    :				str       		=							None

          lowercase    :				Optional[Any]       		=							decay
          lowercase    :				Union[str, Any]       		=							min_decay
          lowercase    :				Tuple       		=							update_after_step
          lowercase    :				Union[str, Any]       		=							use_ema_warmup
          lowercase    :				str       		=							inv_gamma
          lowercase    :				int       		=							power
          lowercase    :				Tuple       		=							0
          lowercase    :				Any       		=							None  # set in `step()`

          lowercase    :				List[str]       		=							model_cls
          lowercase    :				Tuple       		=							model_config
   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    _a				):
          lowercase    :				Union[str, Any]       		=							model_cls.load_config(_a	,    return_unused_kwargs=_a				)
          lowercase    :				Dict       		=							model_cls.from_pretrained(_a				)

          lowercase    :				List[str]       		=							cls(model.parameters()	,    model_cls=_a	,    model_config=model.config				)

          ema_model.load_state_dict(_a				)
          return ema_model
   def 			__magic_name__			(			self	,    _a				):






          if self.model_cls is None:
                 raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."				)

          if self.model_config is None:
                 raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."				)

          lowercase    :				str       		=							self.model_cls.from_config(self.model_config				)
          lowercase    :				List[str]       		=							self.state_dict()
          state_dict.pop("shadow_params"	,    _a				)

          model.register_to_config(**_a				)
          self.copy_to(model.parameters()				)
          model.save_pretrained(_a				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Tuple       		=							max(0	,    optimization_step - self.update_after_step - 1				)

          if step <= 0:
                 return 0.0

          if self.use_ema_warmup:
                 lowercase    :				Optional[int]       		=							1 - (1 + step / self.inv_gamma) ** -self.power
          else:
                 lowercase    :				List[Any]       		=							(1 + step) / (10 + step)

          lowercase    :				Tuple       		=							min(_a	,    self.decay				)
          # make sure decay is not smaller than min_decay
          lowercase    :				str       		=							max(_a	,    self.min_decay				)
          return cur_decay_value
   @torch.no_grad()
   def 			__magic_name__			(			self	,    _a				):






          if isinstance(_a	,    torch.nn.Module				):
                 lowercase    :				str       		=							(
                     'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
                     'Please pass the parameters of the module instead.'
                 )
                 deprecate(
                     "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"	,    "1.0.0"	,    _a	,    standard_warn=_a	,    )
                 lowercase    :				Tuple       		=							parameters.parameters()

          lowercase    :				Optional[Any]       		=							list(_a				)

          self.optimization_step += 1

          # Compute the decay factor for the exponential moving average.
          lowercase    :				int       		=							self.get_decay(self.optimization_step				)
          lowercase    :				Dict       		=							decay
          lowercase    :				int       		=							1 - decay

          lowercase    :				Union[str, Any]       		=							contextlib.nullcontext
          if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                 import deepspeed

          for s_param, param in zip(self.shadow_params	,    _a				):
                 if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                        lowercase    :				Union[str, Any]       		=							deepspeed.zero.GatheredParameters(_a	,    modifier_rank=_a				)

                 with context_manager():
                        if param.requires_grad:
                               s_param.sub_(one_minus_decay * (s_param - param)				)
                        else:
                               s_param.copy_(_a				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				str       		=							list(_a				)
          for s_param, param in zip(self.shadow_params	,    _a				):
                 param.data.copy_(s_param.to(param.device				).data				)
   def 			__magic_name__			(			self	,    _a=None	,    _a=None				):
          lowercase    :				str       		=							[
              p.to(device=_a	,    dtype=_a				) if p.is_floating_point() else p.to(device=_a				)
              for p in self.shadow_params
          ]
   def 			__magic_name__			(			self				):
          return {
              "decay": self.decay,
              "min_decay": self.min_decay,
              "optimization_step": self.optimization_step,
              "update_after_step": self.update_after_step,
              "use_ema_warmup": self.use_ema_warmup,
              "inv_gamma": self.inv_gamma,
              "power": self.power,
              "shadow_params": self.shadow_params,
          }
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							[param.detach().cpu().clone() for param in parameters]
   def 			__magic_name__			(			self	,    _a				):






          if self.temp_stored_params is None:
                 raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"				)
          for c_param, param in zip(self.temp_stored_params	,    _a				):
                 param.data.copy_(c_param.data				)

          # Better memory-wise.
          lowercase    :				Union[str, Any]       		=							None

   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							copy.deepcopy(_a				)

          lowercase    :				Optional[int]       		=							state_dict.get("decay"	,    self.decay				)
          if self.decay < 0.0 or self.decay > 1.0:
                 raise ValueError("Decay must be between 0 and 1"				)

          lowercase    :				List[Any]       		=							state_dict.get("min_decay"	,    self.min_decay				)
          if not isinstance(self.min_decay	,    _a				):
                 raise ValueError("Invalid min_decay"				)

          lowercase    :				str       		=							state_dict.get("optimization_step"	,    self.optimization_step				)
          if not isinstance(self.optimization_step	,    _a				):
                 raise ValueError("Invalid optimization_step"				)

          lowercase    :				Optional[int]       		=							state_dict.get("update_after_step"	,    self.update_after_step				)
          if not isinstance(self.update_after_step	,    _a				):
                 raise ValueError("Invalid update_after_step"				)

          lowercase    :				str       		=							state_dict.get("use_ema_warmup"	,    self.use_ema_warmup				)
          if not isinstance(self.use_ema_warmup	,    _a				):
                 raise ValueError("Invalid use_ema_warmup"				)

          lowercase    :				int       		=							state_dict.get("inv_gamma"	,    self.inv_gamma				)
          if not isinstance(self.inv_gamma	,    (float, int)				):
                 raise ValueError("Invalid inv_gamma"				)

          lowercase    :				Optional[Any]       		=							state_dict.get("power"	,    self.power				)
          if not isinstance(self.power	,    (float, int)				):
                 raise ValueError("Invalid power"				)

          lowercase    :				Union[str, Any]       		=							state_dict.get("shadow_params"	,    _a				)
          if shadow_params is not None:
                 lowercase    :				Union[str, Any]       		=							shadow_params
                 if not isinstance(self.shadow_params	,    _a				):
                        raise ValueError("shadow_params must be a list"				)
                 if not all(isinstance(_a	,    torch.Tensor				) for p in self.shadow_params				):
                        raise ValueError("shadow_params must all be Tensors"				)




'''simple docstring'''







import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					inspect.getfile(accelerate.test_utils	)
  __UpperCAmelCase				:				List[str]   =					os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['''scripts''', '''test_cli.py''']	)

  __UpperCAmelCase				:				Dict   =					['''accelerate''', '''launch''']
  __UpperCAmelCase				:				Dict   =					Path.home() / '''.cache/huggingface/accelerate'''
  __UpperCAmelCase				:				Dict   =					'''default_config.yaml'''
  __UpperCAmelCase				:				Optional[Any]   =					config_folder / config_file
  __UpperCAmelCase				:				Dict   =					config_folder / '''_default_config.yaml'''

  __UpperCAmelCase				:				Any   =					Path('''tests/test_configs'''	)


  @classmethod
  def   __lowercase		(		cls      :     int       ):


     '''simple docstring'''






     if cls.config_path.is_file():
        cls.config_path.rename(cls.changed_path       )


  @classmethod
  def   __lowercase		(		cls      :     List[Any]       ):


     '''simple docstring'''






     if cls.changed_path.is_file():
        cls.changed_path.rename(cls.config_path       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Dict			=   self.base_cmd
     if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
        cmd += ["--multi_gpu"]
     execute_subprocess_async(cmd + [self.test_file_path]							,env=os.environ.copy()       )


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     for config in sorted(self.test_config_path.glob('**/*.yaml'       )       ):
        with self.subTest(config_file=_a       ):
           execute_subprocess_async(
               self.base_cmd + ['--config_file', str(_a       ), self.test_file_path]							,env=os.environ.copy()       )


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     execute_subprocess_async(['accelerate', 'test']							,env=os.environ.copy()       )







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					'''test-tpu'''
  __UpperCAmelCase				:				Any   =					'''us-central1-a'''
  __UpperCAmelCase				:				List[Any]   =					'''ls'''
  __UpperCAmelCase				:				Any   =					['''accelerate''', '''tpu-config''']
  __UpperCAmelCase				:				Dict   =					'''cd /usr/share'''
  __UpperCAmelCase				:				Any   =					'''tests/test_samples/test_command_file.sh'''
  __UpperCAmelCase				:				List[Any]   =					'''Running gcloud compute tpus tpu-vm ssh'''


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   run_command(
         self.cmd
         + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""							,_a							,)


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Any			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command',
             self.command,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""							,_a							,)


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']							,return_stdout=_a       )
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     int       ):


     '''simple docstring'''






     _a				:	Optional[Any]			=   run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""							,_a							,)


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     _a				:	List[str]			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--command',
             self.command,
             '--command',
             'echo "Hello World"',
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Any			=   run_command(
         self.cmd
         + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command_file',
             self.command_file,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     Any       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)


  def   __lowercase		(		self      :     List[str]       ):


     '''simple docstring'''






     _a				:	Optional[int]			=   run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--install_accelerate',
             '--accelerate_version',
             '12.0.0',
             '--debug',
         ]							,return_stdout=_a							,)
     self.assertIn(
         F"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""							,_a							,)



