



"""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      UpperCAmelCase__		(      tf.keras.optimizers.schedules.LearningRateSchedule     ):



		def __init__( self  :    Any      ,							snake_case  :    float      ,							snake_case  :    Callable      ,							snake_case  :    int      ,							snake_case  :    float = 1.0      ,							snake_case  :    str = None      ,							)			->						int:






									'''simple docstring'''

									super().__init__()
									A         =					initial_learning_rate
									A         =					warmup_steps
									A         =					power
									A         =					decay_schedule_fn
									A         =					name



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






									'''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`.
																A         =					tf.cast(snake_case      ,							tf.floataa				)
																A         =					tf.cast(self.warmup_steps      ,							tf.floataa				)
																A         =					global_step_float / warmup_steps_float
																A         =					self.initial_learning_rate * tf.math.pow(snake_case      ,							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=snake_case      ,							)



		def 					A_		( self  :    int				)			->						Optional[int]:






									'''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   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 0.0				,      lowerCamelCase__ = 0.9				,      lowerCamelCase__ = 0.9_99				,      lowerCamelCase__ = 1E-8				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = 0.0				,      lowerCamelCase__ = 1.0				,      lowerCamelCase__ = None				,      ) ->							List[str]:
							A         =					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         =					WarmUp(
														    initial_learning_rate=lowerCamelCase__				,      decay_schedule_fn=lowerCamelCase__				,      warmup_steps=lowerCamelCase__				,      )
							if weight_decay_rate > 0.0:
														A         =					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         =					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      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    Optional[int]      ,							snake_case  :    Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001      ,							snake_case  :    float = 0.9      ,							snake_case  :    float = 0.999      ,							snake_case  :    float = 1E-7      ,							snake_case  :    bool = False      ,							snake_case  :    float = 0.0      ,							snake_case  :    Optional[List[str]] = None      ,							snake_case  :    Optional[List[str]] = None      ,							snake_case  :    str = "AdamWeightDecay"      ,							**snake_case  :    Union[str, Any]      ,							)			->						Union[str, Any]:






									'''simple docstring'''

									super().__init__(snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							**snake_case				)
									A         =					weight_decay_rate
									A         =					include_in_weight_decay
									A         =					exclude_from_weight_decay



		@classmethod
		def 					A_		( cls  :    Any      ,							snake_case  :    str				)			->						Optional[int]:






									'''simple docstring'''

									A         =					{'WarmUp': WarmUp}
									return super(snake_case      ,							cls				).from_config(snake_case      ,							custom_objects=snake_case				)



		def 					A_		( self  :    Dict      ,							snake_case  :    Optional[int]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Union[str, Any]				)			->						Tuple:






									'''simple docstring'''

									super(snake_case      ,							self				)._prepare_local(snake_case      ,							snake_case      ,							snake_case				)
									A         =					tf.constant(
									    self.weight_decay_rate      ,							name='adam_weight_decay_rate'				)



		def 					A_		( self  :    Dict      ,							snake_case  :    Optional[int]      ,							snake_case  :    int      ,							snake_case  :    Dict				)			->						Any:






									'''simple docstring'''

									A         =					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 					A_		( self  :    List[str]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Dict=None      ,							**snake_case  :    str				)			->						Tuple:






									'''simple docstring'''

									A      ,  A         =					list(zip(*snake_case				)				)
									return super(snake_case      ,							self				).apply_gradients(zip(snake_case      ,							snake_case				)      ,							name=snake_case      ,							**snake_case				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    Optional[int]      ,							snake_case  :    List[str]      ,							snake_case  :    List[Any]				)			->						int:






									'''simple docstring'''

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

									A         =					apply_state or {}
									A         =					apply_state.get((var_device, var_dtype)				)
									if coefficients is None:
																A         =					self._fallback_apply_state(snake_case      ,							snake_case				)
																A         =					coefficients

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



		def 					A_		( self  :    Dict      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    str      ,							snake_case  :    str=None				)			->						int:






									'''simple docstring'''

									A      ,  A         =					self._get_lr(var.device      ,							var.dtype.base_dtype      ,							snake_case				)
									A         =					self._decay_weights_op(snake_case      ,							snake_case      ,							snake_case				)
									with tf.control_dependencies([decay]				):
																return super(snake_case      ,							self				)._resource_apply_dense(snake_case      ,							snake_case      ,							**snake_case				)



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]      ,							snake_case  :    Optional[int]      ,							snake_case  :    Any=None				)			->						str:






									'''simple docstring'''

									A      ,  A         =					self._get_lr(var.device      ,							var.dtype.base_dtype      ,							snake_case				)
									A         =					self._decay_weights_op(snake_case      ,							snake_case      ,							snake_case				)
									with tf.control_dependencies([decay]				):
																return super(snake_case      ,							self				)._resource_apply_sparse(snake_case      ,							snake_case      ,							snake_case      ,							**snake_case				)



		def 					A_		( self  :    Optional[Any]				)			->						str:






									'''simple docstring'''

									A         =					super().get_config()
									config.update({'weight_decay_rate': self.weight_decay_rate}				)
									return config



		def 					A_		( self  :    Dict      ,							snake_case  :    Union[str, Any]				)			->						Tuple:






									'''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(snake_case      ,							snake_case				) is not None:
																														return True

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






class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    Any				)			->						int:






									'''simple docstring'''

									A         =					[]
									A         =					None



		@property
		def 					A_		( self  :    List[Any]				)			->						Optional[int]:






									'''simple docstring'''

									if self._accum_steps is None:
																A         =					tf.Variable(
																    tf.constant(0      ,							dtype=tf.intaa				)      ,							trainable=snake_case      ,							synchronization=tf.VariableSynchronization.ON_READ      ,							aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,							)

									return self._accum_steps.value()



		@property
		def 					A_		( self  :    Any				)			->						Optional[int]:






									'''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  :    Tuple      ,							snake_case  :    List[str]				)			->						Dict:






									'''simple docstring'''

									if not self._gradients:
																A         =					self.step  # Create the step variable.
																self._gradients.extend(
																    [
																        tf.Variable(
																            tf.zeros_like(snake_case				)      ,							trainable=snake_case      ,							synchronization=tf.VariableSynchronization.ON_READ      ,							aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA      ,							)
																        if gradient is not None
																        else gradient
																        for gradient in gradients
																    ]				)
									if len(snake_case				) != len(self._gradients				):
																raise ValueError(f"""Expected {len(self._gradients				)} gradients, but got {len(snake_case				)}"""				)

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

									self._accum_steps.assign_add(1				)



		def 					A_		( self  :    Dict				)			->						str:






									'''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(snake_case				)				)








"""simple docstring"""







import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
		lowerCAmelCase_       :	Tuple 							=     LayoutLMTokenizer
		lowerCAmelCase_       :	Any 							=     LayoutLMTokenizerFast
		lowerCAmelCase_       :	Optional[int] 							=     True
		lowerCAmelCase_       :	List[Any] 							=     True



		def 					A_		( self  :    Optional[Any]				)			->						Optional[Any]:






									'''simple docstring'''

									super().setUp()

									A         =					[
									    '[UNK]',
									    '[CLS]',
									    '[SEP]',
									    'want',
									    '##want',
									    '##ed',
									    'wa',
									    'un',
									    'runn',
									    '##ing',
									    ',',
									    'low',
									    'lowest',
									]
									A         =					os.path.join(self.tmpdirname      ,							VOCAB_FILES_NAMES['vocab_file']				)
									with open(self.vocab_file      ,							'w'      ,							encoding='utf-8'				) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]				)				)



		def 					A_		( self  :    str      ,							**snake_case  :    Tuple				)			->						Union[str, Any]:






									'''simple docstring'''

									return LayoutLMTokenizer.from_pretrained(self.tmpdirname      ,							**snake_case				)



		def 					A_		( self  :    List[str]      ,							snake_case  :    int				)			->						List[Any]:






									'''simple docstring'''

									A         =					'UNwant\u00E9d,running'
									A         =					'unwanted, running'
									return input_text, output_text



		def 					A_		( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.tokenizer_class(self.vocab_file				)

									A         =					tokenizer.tokenize('UNwant\u00E9d,running'				)
									self.assertListEqual(snake_case      ,							['un', '##want', '##ed', ',', 'runn', '##ing']				)
									self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case				)      ,							[7, 4, 5, 10, 8, 9]				)



		def 					A_		( self  :    Any				)			->						List[Any]:






									'''simple docstring'''

									pass



