



'''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 	__magic_name__						(    tf.keras.optimizers.schedules.LearningRateSchedule   ):







							def __init__(							self					:       Tuple	,_UpperCAmelCase					:       float	,_UpperCAmelCase					:       Callable	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       float = 1.0	,_UpperCAmelCase					:       str = None	,):
									super().__init__()
									_a     :      List[Any]        =							initial_learning_rate
									_a     :      Optional[int]        =							warmup_steps
									_a     :      List[str]        =							power
									_a     :      Dict        =							decay_schedule_fn
									_a     :      List[Any]        =							name







							def __call__(							self					:       Optional[int]	,_UpperCAmelCase					:       Dict		):
									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`.
											_a     :      str        =							tf.cast(_UpperCAmelCase	,tf.floataa		)
											_a     :      List[str]        =							tf.cast(self.warmup_steps	,tf.floataa		)
											_a     :      Tuple        =							global_step_float / warmup_steps_float
											_a     :      Optional[int]        =							self.initial_learning_rate * tf.math.pow(_UpperCAmelCase	,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=_UpperCAmelCase	,)







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







							def __init__(							self					:       str	,_UpperCAmelCase					:       Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.0_01	,_UpperCAmelCase					:       float = 0.9	,_UpperCAmelCase					:       float = 0.9_99	,_UpperCAmelCase					:       float = 1E-7	,_UpperCAmelCase					:       bool = False	,_UpperCAmelCase					:       float = 0.0	,_UpperCAmelCase					:       Optional[List[str]] = None	,_UpperCAmelCase					:       Optional[List[str]] = None	,_UpperCAmelCase					:       str = "AdamWeightDecay"	,**_UpperCAmelCase					:       int	,):
									super().__init__(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      List[Any]        =							weight_decay_rate
									_a     :      int        =							include_in_weight_decay
									_a     :      str        =							exclude_from_weight_decay







							@classmethod
							def 							__lowercase			(							cls					:       Any	,_UpperCAmelCase					:       int		):
									_a     :      int        =							{'WarmUp': WarmUp}
									return super(_UpperCAmelCase	,cls		).from_config(_UpperCAmelCase	,custom_objects=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       List[str]	,_UpperCAmelCase					:       Tuple	,_UpperCAmelCase					:       Union[str, Any]		):
									super(_UpperCAmelCase	,self		)._prepare_local(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)
									_a     :      str        =							tf.constant(
									    self.weight_decay_rate	,name='adam_weight_decay_rate'		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       List[str]		):
									_a     :      Dict        =							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					:       Optional[Any]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       List[str]=None	,**_UpperCAmelCase					:       Optional[int]		):
									_a       ,					_a     :      int        =							list(zip(*_UpperCAmelCase		)		)
									return super(_UpperCAmelCase	,self		).apply_gradients(zip(_UpperCAmelCase	,_UpperCAmelCase		)	,name=_UpperCAmelCase	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       List[Any]	,_UpperCAmelCase					:       Union[str, Any]		):
									if apply_state is None:
											return self._decayed_lr_t[var_dtype], {}

									_a     :      Union[str, Any]        =							apply_state or {}
									_a     :      Union[str, Any]        =							apply_state.get((var_device, var_dtype)		)
									if coefficients is None:
											_a     :      Any        =							self._fallback_apply_state(_UpperCAmelCase	,_UpperCAmelCase		)
											_a     :      Any        =							coefficients

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







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Optional[int]	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       int=None		):
									_a       ,					_a     :      str        =							self._get_lr(var.device	,var.dtype.base_dtype	,_UpperCAmelCase		)
									_a     :      Any        =							self._decay_weights_op(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)
									with tf.control_dependencies([decay]		):
											return super(_UpperCAmelCase	,self		)._resource_apply_dense(_UpperCAmelCase	,_UpperCAmelCase	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Tuple	,_UpperCAmelCase					:       int	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Dict	,_UpperCAmelCase					:       List[Any]=None		):
									_a       ,					_a     :      Tuple        =							self._get_lr(var.device	,var.dtype.base_dtype	,_UpperCAmelCase		)
									_a     :      Tuple        =							self._decay_weights_op(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)
									with tf.control_dependencies([decay]		):
											return super(_UpperCAmelCase	,self		)._resource_apply_sparse(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      List[Any]        =							super().get_config()
									config.update({'weight_decay_rate': self.weight_decay_rate}		)
									return config







							def 							__lowercase			(							self					:       List[Any]	,_UpperCAmelCase					:       Dict		):
									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(_UpperCAmelCase	,_UpperCAmelCase		) is not None:
															return True

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

class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       List[str]		):
									_a     :      Optional[Any]        =							[]
									_a     :      Any        =							None







							@property
							def 							__lowercase			(							self					:       int		):
									if self._accum_steps is None:
											_a     :      List[str]        =							tf.Variable(
											    tf.constant(0	,dtype=tf.intaa		)	,trainable=_UpperCAmelCase	,synchronization=tf.VariableSynchronization.ON_READ	,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA	,)

									return self._accum_steps.value()







							@property
							def 							__lowercase			(							self					:       str		):
									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					:       int	,_UpperCAmelCase					:       Union[str, Any]		):
									if not self._gradients:
											_a     :      Dict        =							self.step  # Create the step variable.
											self._gradients.extend(
											    [
											        tf.Variable(
											            tf.zeros_like(_UpperCAmelCase		)	,trainable=_UpperCAmelCase	,synchronization=tf.VariableSynchronization.ON_READ	,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA	,)
											        if gradient is not None
											        else gradient
											        for gradient in gradients
											    ]		)
									if len(_UpperCAmelCase		) != len(self._gradients		):
											raise ValueError(F"""Expected {len(self._gradients		)} gradients, but got {len(_UpperCAmelCase		)}"""		)

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

									self._accum_steps.assign_add(1		)







							def 							__lowercase			(							self					:       List[str]		):
									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(_UpperCAmelCase		)		)









'''simple docstring'''

import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__lowerCAmelCase									=   logging.getLogger()

@unittest.skip('Temporarily disable the doc tests.'   )
@require_torch
@require_tf
@slow
class 	__magic_name__						(    unittest.TestCase   ):







							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       Path	,_UpperCAmelCase					:       Union[str, None] = None	,_UpperCAmelCase					:       Union[List[str], None] = None	,_UpperCAmelCase					:       Union[str, List[str], None] = None	,_UpperCAmelCase					:       bool = True	,):
									_a     :      Dict        =							[file for file in os.listdir(_UpperCAmelCase		) if os.path.isfile(os.path.join(_UpperCAmelCase	,_UpperCAmelCase		)		)]

									if identifier is not None:
											_a     :      str        =							[file for file in files if identifier in file]

									if n_identifier is not None:
											if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													for n_ in n_identifier:
															_a     :      int        =							[file for file in files if n_ not in file]
											else:
													_a     :      Optional[Any]        =							[file for file in files if n_identifier not in file]

									_a     :      Dict        =							ignore_files or []
									ignore_files.append('__init__.py'		)
									_a     :      List[str]        =							[file for file in files if file not in ignore_files]

									for file in files:
											# Open all files
											print('Testing'	,_UpperCAmelCase		)

											if only_modules:
													_a     :      Any        =							file.split('.'		)[0]
													try:
															_a     :      Optional[int]        =							getattr(_UpperCAmelCase	,_UpperCAmelCase		)
															_a     :      Dict        =							doctest.DocTestSuite(_UpperCAmelCase		)
															_a     :      Optional[int]        =							unittest.TextTestRunner().run(_UpperCAmelCase		)
															self.assertIs(len(result.failures		)	,0		)
													except AttributeError:
															logger.info(F"""{module_identifier} is not a module."""		)
											else:
													_a     :      str        =							doctest.testfile(str('..' / directory / file		)	,optionflags=doctest.ELLIPSIS		)
													self.assertIs(result.failed	,0		)







							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Optional[Any]        =							Path('src/transformers'		)
									_a     :      Optional[Any]        =							'modeling'
									_a     :      Union[str, Any]        =							[
									    'modeling_ctrl.py',
									    'modeling_tf_ctrl.py',
									]
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase	,ignore_files=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      str        =							Path('src/transformers'		)
									_a     :      List[str]        =							'tokenization'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       int		):
									_a     :      Any        =							Path('src/transformers'		)
									_a     :      str        =							'configuration'
									self.analyze_directory(_UpperCAmelCase	,identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Tuple        =							Path('src/transformers'		)
									_a     :      Optional[int]        =							['configuration', 'modeling', 'tokenization']
									self.analyze_directory(_UpperCAmelCase	,n_identifier=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									_a     :      Union[str, Any]        =							Path('docs/source'		)
									_a     :      List[str]        =							['favicon.ico']
									self.analyze_directory(_UpperCAmelCase	,ignore_files=_UpperCAmelCase	,only_modules=_UpperCAmelCase		)




