




import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
 from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
 from tensorflow.keras.optimizers import Adam


class    lowerCamelCase    (		tf.keras.optimizers.schedules.LearningRateSchedule						):






   '''simple docstring'''





   def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   float       ,						lowerCamelCase_			:   Callable       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   float = 1.0       ,						lowerCamelCase_			:   str = None       ,						)						->   Optional[int]:
        super().__init__()
        __magic_name__							:	Union[str, Any]    							=							initial_learning_rate
        __magic_name__							:	Optional[int]    							=							warmup_steps
        __magic_name__							:	Union[str, Any]    							=							power
        __magic_name__							:	List[Any]    							=							decay_schedule_fn
        __magic_name__							:	Dict    							=							name

   def __call__( self			:   Any       ,						lowerCamelCase_			:   Dict  )						->   Optional[Any]:
        with tf.name_scope(self.name or '''WarmUp'''  ) as name:
             # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
             # learning rate will be `global_step/num_warmup_steps * init_lr`.
             __magic_name__							:	Union[str, Any]    							=							tf.cast(lowerCamelCase_       ,						tf.floataa  )
             __magic_name__							:	Dict    							=							tf.cast(self.warmup_steps       ,						tf.floataa  )
             __magic_name__							:	Dict    							=							global_step_float / warmup_steps_float
             __magic_name__							:	List[str]    							=							self.initial_learning_rate * tf.math.pow(lowerCamelCase_       ,						self.power  )
             return tf.cond(
                 global_step_float < warmup_steps_float       ,						lambda: warmup_learning_rate       ,						lambda: self.decay_schedule_fn(step - self.warmup_steps  )       ,						name=lowerCamelCase_       ,						)


   def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   str:
        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }







def 		lowercase__   (		__A:	float							,__A:	int							,__A:	int							,__A:	float = 0.0							,__A:	float = 0.9							,__A:	float = 0.999							,__A:	float = 1e-8							,__A:	Optional[float] = None							,__A:	Optional[float] = None							,__A:	float = 0.0							,__A:	float = 1.0							,__A:	Optional[List[str]] = None							,):





     '''simple docstring'''






     __magic_name__							:	Any    							=							tf.keras.optimizers.schedules.PolynomialDecay(
         initial_learning_rate=__A							,decay_steps=num_train_steps - num_warmup_steps							,end_learning_rate=init_lr * min_lr_ratio							,power=__A							,)
     if num_warmup_steps:
          __magic_name__							:	Tuple    							=							WarmUp(
              initial_learning_rate=__A							,decay_schedule_fn=__A							,warmup_steps=__A							,)
     if weight_decay_rate > 0.0:
          __magic_name__							:	Union[str, Any]    							=							AdamWeightDecay(
              learning_rate=__A							,weight_decay_rate=__A							,beta_a=__A							,beta_a=__A							,epsilon=__A							,clipnorm=__A							,global_clipnorm=__A							,exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']							,include_in_weight_decay=__A							,)
     else:
          __magic_name__							:	str    							=							tf.keras.optimizers.Adam(
              learning_rate=__A							,beta_a=__A							,beta_a=__A							,epsilon=__A							,clipnorm=__A							,global_clipnorm=__A							,)
     # We return the optimizer and the LR scheduler in order to better track the
     # evolution of the LR independently of the optimizer.
     return optimizer, lr_schedule


class    lowerCamelCase    (		_lowerCamelCase						):






   '''simple docstring'''





   def __init__( self			:   Optional[int]       ,						lowerCamelCase_			:   Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_0_1       ,						lowerCamelCase_			:   float = 0.9       ,						lowerCamelCase_			:   float = 0.9_9_9       ,						lowerCamelCase_			:   float = 1E-7       ,						lowerCamelCase_			:   bool = False       ,						lowerCamelCase_			:   float = 0.0       ,						lowerCamelCase_			:   Optional[List[str]] = None       ,						lowerCamelCase_			:   Optional[List[str]] = None       ,						lowerCamelCase_			:   str = "AdamWeightDecay"       ,						**lowerCamelCase_			:   int       ,						)						->   List[str]:
        super().__init__(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )
        __magic_name__							:	int    							=							weight_decay_rate
        __magic_name__							:	Tuple    							=							include_in_weight_decay
        __magic_name__							:	Union[str, Any]    							=							exclude_from_weight_decay

   @classmethod
   def 							UpperCAmelCase__       ( cls			:   int       ,						lowerCamelCase_			:   int  )						->   Optional[Any]:
        __magic_name__							:	Tuple    							=							{'''WarmUp''': WarmUp}
        return super(lowerCamelCase_       ,						cls  ).from_config(lowerCamelCase_       ,						custom_objects=lowerCamelCase_  )

   def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   Optional[int]  )						->   int:
        super(lowerCamelCase_       ,						self  )._prepare_local(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
        __magic_name__							:	Any    							=							tf.constant(
            self.weight_decay_rate       ,						name='''adam_weight_decay_rate'''  )

   def 							UpperCAmelCase__       ( self			:   Optional[int]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Any  )						->   Optional[Any]:
        __magic_name__							:	Optional[int]    							=							self._do_use_weight_decay(var.name  )
        if do_decay:
             return var.assign_sub(
                 learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate''']       ,						use_locking=self._use_locking       ,						)
        return tf.no_op()

   def 							UpperCAmelCase__       ( self			:   str       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int=None       ,						**lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
        __magic_name__							:	Dict    							=							list(zip(*lowerCamelCase_  )  )
        return super(lowerCamelCase_       ,						self  ).apply_gradients(zip(lowerCamelCase_       ,						lowerCamelCase_  )       ,						name=lowerCamelCase_       ,						**lowerCamelCase_  )

   def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[int]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
        if apply_state is None:
             return self._decayed_lr_t[var_dtype], {}

        __magic_name__							:	str    							=							apply_state or {}
        __magic_name__							:	Optional[Any]    							=							apply_state.get((var_device, var_dtype)  )
        if coefficients is None:
             __magic_name__							:	List[str]    							=							self._fallback_apply_state(lowerCamelCase_       ,						lowerCamelCase_  )
             __magic_name__							:	List[Any]    							=							coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}

   def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   List[str]       ,						lowerCamelCase_			:   Dict=None  )						->   Optional[Any]:
        __magic_name__							:	Union[str, Any]    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
        __magic_name__							:	Optional[Any]    							=							self._decay_weights_op(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
        with tf.control_dependencies([decay]  ):
             return super(lowerCamelCase_       ,						self  )._resource_apply_dense(lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )

   def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   Union[str, Any]=None  )						->   Any:
        __magic_name__							:	Tuple    							=							self._get_lr(var.device       ,						var.dtype.base_dtype       ,						lowerCamelCase_  )
        __magic_name__							:	Optional[int]    							=							self._decay_weights_op(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )
        with tf.control_dependencies([decay]  ):
             return super(lowerCamelCase_       ,						self  )._resource_apply_sparse(lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						**lowerCamelCase_  )

   def 							UpperCAmelCase__       ( self			:   int  )						->   Dict:
        __magic_name__							:	List[Any]    							=							super().get_config()
        config.update({'''weight_decay_rate''': self.weight_decay_rate}  )
        return config


   def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Union[str, Any]:
        if self.weight_decay_rate == 0:
             return False

        if self._include_in_weight_decay:
             for r in self._include_in_weight_decay:
                  if re.search(lowerCamelCase_       ,						lowerCamelCase_  ) is not None:
                       return True

        if self._exclude_from_weight_decay:
             for r in self._exclude_from_weight_decay:
                  if re.search(lowerCamelCase_       ,						lowerCamelCase_  ) is not None:
                       return False
        return True






class    lowerCamelCase    (		_lowerCamelCase						):






   '''simple docstring'''





   def __init__( self			:   Optional[Any]  )						->   Any:
        __magic_name__							:	Optional[int]    							=							[]
        __magic_name__							:	Dict    							=							None

   @property
   def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
        if self._accum_steps is None:
             __magic_name__							:	Optional[Any]    							=							tf.Variable(
                 tf.constant(0       ,						dtype=tf.intaa  )       ,						trainable=lowerCamelCase_       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)

        return self._accum_steps.value()

   @property
   def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[Any]:
        if not self._gradients:
             raise ValueError('''The accumulator should be called first to initialize the gradients'''  )
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]

   def __call__( self			:   Optional[int]       ,						lowerCamelCase_			:   Any  )						->   List[str]:
        if not self._gradients:
             __magic_name__							:	int    							=							self.step  # Create the step variable.
             self._gradients.extend(
                 [
                     tf.Variable(
                         tf.zeros_like(lowerCamelCase_  )       ,						trainable=lowerCamelCase_       ,						synchronization=tf.VariableSynchronization.ON_READ       ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,						)
                     if gradient is not None
                     else gradient
                     for gradient in gradients
                 ]  )
        if len(lowerCamelCase_  ) != len(self._gradients  ):
             raise ValueError(F'''Expected {len(self._gradients  )} gradients, but got {len(lowerCamelCase_  )}'''  )

        for accum_gradient, gradient in zip(self._gradients       ,						lowerCamelCase_  ):
             if accum_gradient is not None and gradient is not None:
                  accum_gradient.assign_add(lowerCamelCase_  )

        self._accum_steps.assign_add(1  )


   def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[int]:
        if not self._gradients:
             return
        self._accum_steps.assign(0  )
        for gradient in self._gradients:
             if gradient is not None:
                  gradient.assign(tf.zeros_like(lowerCamelCase_  )  )









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    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





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

			UpperCamelCase__								=['''accelerate''', '''launch''']
			UpperCamelCase__								=Path.home() / '''.cache/huggingface/accelerate'''
			UpperCamelCase__								='''default_config.yaml'''
			UpperCamelCase__								=config_folder / config_file
			UpperCamelCase__								=config_folder / '''_default_config.yaml'''

			UpperCamelCase__								=Path('''tests/test_configs'''						)

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Optional[int]  )						->   int:
								if cls.config_path.is_file():
													cls.config_path.rename(cls.changed_path  )

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Dict  )						->   List[str]:
								if cls.changed_path.is_file():
													cls.changed_path.rename(cls.config_path  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   Optional[Any]:
								__magic_name__							:	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 							UpperCAmelCase__       ( self			:   int  )						->   Optional[int]:
								for config in sorted(self.test_config_path.glob('''**/*.yaml'''  )  ):
													with self.subTest(config_file=lowerCamelCase_  ):
																		execute_subprocess_async(
																		    self.base_cmd + ['''--config_file''', str(lowerCamelCase_  ), self.test_file_path]       ,						env=os.environ.copy()  )


			def 							UpperCAmelCase__       ( self			:   int  )						->   Union[str, Any]:
								execute_subprocess_async(['''accelerate''', '''test''']       ,						env=os.environ.copy()  )






class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





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

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

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   int:
								__magic_name__							:	int    							=							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=lowerCamelCase_       ,						)
								self.assertIn(
								    F'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   Tuple:
								__magic_name__							:	Any    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug''']       ,						return_stdout=lowerCamelCase_  )
								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'''       ,						lowerCamelCase_       ,						)

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

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

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							run_command(
								    self.cmd
								    + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   int  )						->   List[Any]:
								__magic_name__							:	List[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=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)

			def 							UpperCAmelCase__       ( self			:   str  )						->   Dict:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug''']       ,						return_stdout=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   Any  )						->   Tuple:
								__magic_name__							:	Tuple    							=							run_command(
								    self.cmd
								    + [
								        '''--config_file''',
								        '''tests/test_configs/latest.yaml''',
								        '''--install_accelerate''',
								        '''--accelerate_version''',
								        '''12.0.0''',
								        '''--debug''',
								    ]       ,						return_stdout=lowerCamelCase_       ,						)
								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'''       ,						lowerCamelCase_       ,						)



