



'''simple docstring'''

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 						a__  (					tf.keras.optimizers.schedules.LearningRateSchedule						):


	def __init__(self			:			Optional[Any],       __UpperCAmelCase			:			str,       __UpperCAmelCase			:			Union[str, Any],       __UpperCAmelCase			:			Optional[Any],       __UpperCAmelCase			:			Optional[int] = 1.0,       __UpperCAmelCase			:			List[Any] = None,       )  ->      Union[str, Any]:

					"""simple docstring"""

					super().__init__()
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			initial_learning_rate
					SCREAMING_SNAKE_CASE	:    Tuple          =			warmup_steps
					SCREAMING_SNAKE_CASE	:    Tuple          =			power
					SCREAMING_SNAKE_CASE	:    Dict          =			decay_schedule_fn
					SCREAMING_SNAKE_CASE	:    str          =			name


	def __call__(self			:			Optional[Any],       __UpperCAmelCase			:			str   )  ->      List[Any]:

					"""simple docstring"""

					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`.
									SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tf.cast(_lowerCamelCase,       tf.floataa   )
									SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tf.cast(self.warmup_steps,       tf.floataa   )
									SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			global_step_float / warmup_steps_float
									SCREAMING_SNAKE_CASE	:    Optional[Any]          =			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 					lowercase__	(self			:			Dict   )  ->      Union[str, Any]:

					"""simple docstring"""

					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	(_SCREAMING_SNAKE_CASE   :float     ,						_SCREAMING_SNAKE_CASE   :int     ,						_SCREAMING_SNAKE_CASE   :int     ,						_SCREAMING_SNAKE_CASE   :float = 0.0     ,						_SCREAMING_SNAKE_CASE   :float = 0.9     ,						_SCREAMING_SNAKE_CASE   :float = 0.999     ,						_SCREAMING_SNAKE_CASE   :float = 1E-8     ,						_SCREAMING_SNAKE_CASE   :Optional[float] = None     ,						_SCREAMING_SNAKE_CASE   :Optional[float] = None     ,						_SCREAMING_SNAKE_CASE   :float = 0.0     ,						_SCREAMING_SNAKE_CASE   :float = 1.0     ,						_SCREAMING_SNAKE_CASE   :Optional[List[str]] = None     ,						):
				SCREAMING_SNAKE_CASE	:    Any          =			tf.keras.optimizers.schedules.PolynomialDecay(
				    initial_learning_rate=UpperCAmelCase_     ,						decay_steps=num_train_steps - num_warmup_steps     ,						end_learning_rate=init_lr * min_lr_ratio     ,						power=UpperCAmelCase_     ,						)
				if num_warmup_steps:
								SCREAMING_SNAKE_CASE	:    Optional[int]          =			WarmUp(
								    initial_learning_rate=UpperCAmelCase_     ,						decay_schedule_fn=UpperCAmelCase_     ,						warmup_steps=UpperCAmelCase_     ,						)
				if weight_decay_rate > 0.0:
								SCREAMING_SNAKE_CASE	:    str          =			AdamWeightDecay(
								    learning_rate=UpperCAmelCase_     ,						weight_decay_rate=UpperCAmelCase_     ,						beta_a=UpperCAmelCase_     ,						beta_a=UpperCAmelCase_     ,						epsilon=UpperCAmelCase_     ,						clipnorm=UpperCAmelCase_     ,						global_clipnorm=UpperCAmelCase_     ,						exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias''']     ,						include_in_weight_decay=UpperCAmelCase_     ,						)
				else:
								SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tf.keras.optimizers.Adam(
								    learning_rate=UpperCAmelCase_     ,						beta_a=UpperCAmelCase_     ,						beta_a=UpperCAmelCase_     ,						epsilon=UpperCAmelCase_     ,						clipnorm=UpperCAmelCase_     ,						global_clipnorm=UpperCAmelCase_     ,						)
				# 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 						a__  (					_snake_case						):


	def __init__(self			:			Any,       __UpperCAmelCase			:			Optional[int] = 0.001,       __UpperCAmelCase			:			str = 0.9,       __UpperCAmelCase			:			Optional[int] = 0.999,       __UpperCAmelCase			:			List[str] = 1e-7,       __UpperCAmelCase			:			Dict = False,       __UpperCAmelCase			:			Tuple = 0.0,       __UpperCAmelCase			:			List[str] = None,       __UpperCAmelCase			:			Union[str, Any] = None,       __UpperCAmelCase			:			List[Any] = "AdamWeightDecay",       **__UpperCAmelCase			:			Union[str, Any],       )  ->      Union[str, Any]:

					"""simple docstring"""

					super().__init__(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       **_lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Dict          =			weight_decay_rate
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			include_in_weight_decay
					SCREAMING_SNAKE_CASE	:    List[str]          =			exclude_from_weight_decay


	@classmethod
	def 					lowercase__	(cls			:			Optional[int],       __UpperCAmelCase			:			int   )  ->      Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    List[str]          =			{'''WarmUp''': WarmUp}
					return super(_lowerCamelCase,       cls   ).from_config(_lowerCamelCase,       custom_objects=_lowerCamelCase   )


	def 					lowercase__	(self			:			Any,       __UpperCAmelCase			:			List[Any],       __UpperCAmelCase			:			int,       __UpperCAmelCase			:			Optional[int]   )  ->      str:

					"""simple docstring"""

					super(_lowerCamelCase,       self   )._prepare_local(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			tf.constant(
					    self.weight_decay_rate,       name='''adam_weight_decay_rate'''   )


	def 					lowercase__	(self			:			List[Any],       __UpperCAmelCase			:			Tuple,       __UpperCAmelCase			:			Optional[Any],       __UpperCAmelCase			:			Union[str, Any]   )  ->      List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    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 					lowercase__	(self			:			Union[str, Any],       __UpperCAmelCase			:			Dict,       __UpperCAmelCase			:			List[str]=None,       **__UpperCAmelCase			:			List[str]   )  ->      str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Optional[int]          =			list(zip(*_lowerCamelCase   )   )
					return super(_lowerCamelCase,       self   ).apply_gradients(zip(_lowerCamelCase,       _lowerCamelCase   ),       name=_lowerCamelCase,       **_lowerCamelCase   )


	def 					lowercase__	(self			:			Dict,       __UpperCAmelCase			:			Optional[Any],       __UpperCAmelCase			:			int,       __UpperCAmelCase			:			int   )  ->      List[Any]:

					"""simple docstring"""

					if apply_state is None:
									return self._decayed_lr_t[var_dtype], {}

					SCREAMING_SNAKE_CASE	:    Tuple          =			apply_state or {}
					SCREAMING_SNAKE_CASE	:    int          =			apply_state.get((var_device, var_dtype)   )
					if coefficients is None:
									SCREAMING_SNAKE_CASE	:    List[str]          =			self._fallback_apply_state(_lowerCamelCase,       _lowerCamelCase   )
									SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			coefficients

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


	def 					lowercase__	(self			:			Union[str, Any],       __UpperCAmelCase			:			Any,       __UpperCAmelCase			:			Union[str, Any],       __UpperCAmelCase			:			List[str]=None   )  ->      Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    int          =			self._get_lr(var.device,       var.dtype.base_dtype,       _lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			self._decay_weights_op(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase   )
					with tf.control_dependencies([decay]   ):
									return super(_lowerCamelCase,       self   )._resource_apply_dense(_lowerCamelCase,       _lowerCamelCase,       **_lowerCamelCase   )


	def 					lowercase__	(self			:			Dict,       __UpperCAmelCase			:			Union[str, Any],       __UpperCAmelCase			:			Any,       __UpperCAmelCase			:			List[Any],       __UpperCAmelCase			:			str=None   )  ->      List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			self._get_lr(var.device,       var.dtype.base_dtype,       _lowerCamelCase   )
					SCREAMING_SNAKE_CASE	:    Optional[Any]          =			self._decay_weights_op(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase   )
					with tf.control_dependencies([decay]   ):
									return super(_lowerCamelCase,       self   )._resource_apply_sparse(_lowerCamelCase,       _lowerCamelCase,       _lowerCamelCase,       **_lowerCamelCase   )


	def 					lowercase__	(self			:			Tuple   )  ->      Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			super().get_config()
					config.update({'''weight_decay_rate''': self.weight_decay_rate}   )
					return config






	def 					lowercase__	(self			:			List[str],       __UpperCAmelCase			:			Optional[Any]   )  ->      List[str]:

					"""simple docstring"""

					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 						a__  (					_snake_case						):


	def __init__(self			:			Tuple   )  ->      str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			[]
					SCREAMING_SNAKE_CASE	:    int          =			None


	@property
	def 					lowercase__	(self			:			Dict   )  ->      Tuple:

					"""simple docstring"""

					if self._accum_steps is None:
									SCREAMING_SNAKE_CASE	:    Tuple          =			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 					lowercase__	(self			:			int   )  ->      Dict:

					"""simple docstring"""

					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			:			Any,       __UpperCAmelCase			:			List[str]   )  ->      Tuple:

					"""simple docstring"""

					if not self._gradients:
									SCREAMING_SNAKE_CASE	:    List[Any]          =			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 					lowercase__	(self			:			str   )  ->      Optional[int]:

					"""simple docstring"""

					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 os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Union[str, Any]=10		):




      """simple docstring"""





      a		:Tuple									=			[]
      for _ in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
      return lrs
def 							__lowerCamelCase		(					UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						Tuple=10		):




      """simple docstring"""





      a		:List[Any]									=			[]
      for step in range(UpperCAmelCase_		):
            lrs.append(scheduler.get_lr()[0]		)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        a		:Union[str, Any]									=			os.path.join(UpperCAmelCase_					,    '''schedule.bin'''		)
                        torch.save(scheduler.state_dict()					,    UpperCAmelCase_		)

                        a		:int									=			torch.load(UpperCAmelCase_		)
                        scheduler.load_state_dict(UpperCAmelCase_		)
      return lrs







@require_torch
class 						_snake_case    ( unittest.TestCase				):




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:int									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:int									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Tuple									=			AdamW(params=[w]   ,    lr=2e-1   ,    weight_decay=0.0       )
       for _ in range(100       ):
             a		:int									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			torch.tensor([0.1, -0.2, -0.1]   ,    requires_grad=_lowerCamelCase       )
       a		:Union[str, Any]									=			torch.tensor([0.4, 0.2, -0.5]       )
       a		:List[Any]									=			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       a		:Any									=			Adafactor(
           params=[w]   ,    lr=1e-2   ,    eps=(1e-30, 1e-3)   ,    clip_threshold=1.0   ,    decay_rate=-0.8   ,    betaa=_lowerCamelCase   ,    weight_decay=0.0   ,    relative_step=_lowerCamelCase   ,    scale_parameter=_lowerCamelCase   ,    warmup_init=_lowerCamelCase   ,    )
       for _ in range(1000       ):
             a		:str									=			criterion(_lowerCamelCase   ,    _lowerCamelCase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()   ,    [0.4, 0.2, -0.5]   ,    tol=1e-2       )







@require_torch
class 						_snake_case    ( unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			nn.Linear(50		,       50				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			AdamW(m.parameters()		,       lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE__ =			10




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       self.assertEqual(len(_lowerCamelCase       )   ,    len(_lowerCamelCase       )       )
       for a, b in zip(_lowerCamelCase   ,    _lowerCamelCase       ):
             self.assertAlmostEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    delta=_lowerCamelCase   ,    msg=_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			{'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       a		:Any									=			{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'''num_warmup_steps''': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, '''num_cycles''': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, '''power''': 2.0, '''lr_end''': 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {'''num_warmup_steps''': 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
             a				,							a		:List[str]									=			data

             a		:str									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )   ,    1       )
             a		:Optional[Any]									=			unwrap_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListAlmostEqual(
                 _lowerCamelCase   ,    _lowerCamelCase   ,    tol=1e-2   ,    msg=F'''failed for {scheduler_func} in normal scheduler'''   ,    )

             a		:List[Any]									=			scheduler_func(self.optimizer   ,    **_lowerCamelCase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowerCamelCase       )  # wrap to test picklability of the schedule
             a		:Dict									=			unwrap_and_save_reload_schedule(_lowerCamelCase   ,    self.num_steps       )
             self.assertListEqual(_lowerCamelCase   ,    _lowerCamelCase   ,    msg=F'''failed for {scheduler_func} in save and reload'''       )







class 						_snake_case    :




 def __init__(						self   ,    _lowerCamelCase       ):
       a		:Dict									=			fn




 def __call__(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       return self.fn(*_lowerCamelCase   ,    **_lowerCamelCase       )




 @classmethod
 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:str									=			list(map(self   ,    scheduler.lr_lambdas       )       )





