


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




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



					'''simple docstring'''







					super().__init__()
					_UpperCAmelCase						:				int	    =					initial_learning_rate
					_UpperCAmelCase						:				Any	    =					warmup_steps
					_UpperCAmelCase						:				Dict	    =					power
					_UpperCAmelCase						:				int	    =					decay_schedule_fn
					_UpperCAmelCase						:				List[str]	    =					name




			def __call__(						self     :   Dict		,     lowerCamelCase__     :   Tuple       )      ->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`.
							_UpperCAmelCase						:				Dict	    =					tf.cast(lowerCamelCase__		,     tf.floataa       )
							_UpperCAmelCase						:				Union[str, Any]	    =					tf.cast(self.warmup_steps		,     tf.floataa       )
							_UpperCAmelCase						:				int	    =					global_step_float / warmup_steps_float
							_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   Tuple       )      ->List[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        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase = 0.0					,       __lowerCAmelCase = 0.9					,       __lowerCAmelCase = 0.9_9_9					,       __lowerCAmelCase = 1e-8					,       __lowerCAmelCase = None					,       __lowerCAmelCase = None					,       __lowerCAmelCase = 0.0					,       __lowerCAmelCase = 1.0					,       __lowerCAmelCase = None					,       ):
		_UpperCAmelCase						:				Union[str, Any]	    =					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:
				_UpperCAmelCase						:				Union[str, Any]	    =					WarmUp(
				    initial_learning_rate=__lowerCAmelCase					,       decay_schedule_fn=__lowerCAmelCase					,       warmup_steps=__lowerCAmelCase					,       )
		if weight_decay_rate > 0.0:
				_UpperCAmelCase						:				int	    =					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:
				_UpperCAmelCase						:				Optional[Any]	    =					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 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   Dict		,     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__     :   List[Any]		,     )      ->Tuple:



					'''simple docstring'''







					super().__init__(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__		,     **lowerCamelCase__       )
					_UpperCAmelCase						:				Optional[int]	    =					weight_decay_rate
					_UpperCAmelCase						:				int	    =					include_in_weight_decay
					_UpperCAmelCase						:				int	    =					exclude_from_weight_decay




			@classmethod
			def 							lowerCAmelCase__			(						cls     :   Tuple		,     lowerCamelCase__     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					{"WarmUp": WarmUp}
					return super(lowerCamelCase__		,     cls       ).from_config(lowerCamelCase__		,     custom_objects=lowerCamelCase__       )




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



					'''simple docstring'''







					super(lowerCamelCase__		,     self       )._prepare_local(lowerCamelCase__		,     lowerCamelCase__		,     lowerCamelCase__       )
					_UpperCAmelCase						:				str	    =					tf.constant(
					    self.weight_decay_rate		,     name="adam_weight_decay_rate"       )




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   Optional[Any]		,     lowerCamelCase__     :   Optional[int]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					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 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   List[Any]=None		,     **lowerCamelCase__     :   Any       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Any	    =					list(zip(*lowerCamelCase__       )       )
					return super(lowerCamelCase__		,     self       ).apply_gradients(zip(lowerCamelCase__		,     lowerCamelCase__       )		,     name=lowerCamelCase__		,     **lowerCamelCase__       )




			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   Any		,     lowerCamelCase__     :   List[Any]		,     lowerCamelCase__     :   int       )      ->Tuple:



					'''simple docstring'''







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

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

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




			def 							lowerCAmelCase__			(						self     :   Union[str, Any]		,     lowerCamelCase__     :   Optional[int]		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   Any=None       )      ->Dict:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   Union[str, Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Any=None       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				int	    =					self._get_lr(var.device		,     var.dtype.base_dtype		,     lowerCamelCase__       )
					_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   Union[str, Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					super().get_config()
					config.update({"weight_decay_rate": self.weight_decay_rate}       )
					return config






			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   str       )      ->Optional[Any]:



					'''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 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   Union[str, Any]       )      ->int:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					[]
					_UpperCAmelCase						:				int	    =					None




			@property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->Tuple:



					'''simple docstring'''







					if self._accum_steps is None:
							_UpperCAmelCase						:				str	    =					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 							lowerCAmelCase__			(						self     :   Union[str, 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     :   Optional[Any]		,     lowerCamelCase__     :   Optional[int]       )      ->str:



					'''simple docstring'''







					if not self._gradients:
							_UpperCAmelCase						:				Tuple	    =					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 							lowerCAmelCase__			(						self     :   Dict       )      ->Tuple:



					'''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__       )       )







'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_fnet import FNetTokenizer
else:
						lowerCamelCase__					=   None

lowerCamelCase__					=   logging.get_logger(__name__)
lowerCamelCase__					=   {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					=   {
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					=   {
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					=   '▁'






class 		lowerCAmelCase__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Dict				      =   VOCAB_FILES_NAMES
			lowerCAmelCase       :		Optional[Any]				      =   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase       :		List[str]				      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase       :		Optional[int]				      =   ["input_ids", "token_type_ids"]
			lowerCAmelCase       :		Optional[Any]				      =   FNetTokenizer




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   int=None		,     lowerCamelCase__     :   List[Any]=None		,     lowerCamelCase__     :   Any=False		,     lowerCamelCase__     :   List[str]=True		,     lowerCamelCase__     :   Dict=True		,     lowerCamelCase__     :   str="<unk>"		,     lowerCamelCase__     :   List[str]="[SEP]"		,     lowerCamelCase__     :   Union[str, Any]="<pad>"		,     lowerCamelCase__     :   Optional[Any]="[CLS]"		,     lowerCamelCase__     :   Any="[MASK]"		,     **lowerCamelCase__     :   Any		,     )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				Dict	    =					(
					    AddedToken(lowerCamelCase__		,     lstrip=lowerCamelCase__		,     rstrip=lowerCamelCase__		,     normalized=lowerCamelCase__       )
					    if isinstance(lowerCamelCase__		,     lowerCamelCase__       )
					    else mask_token
					)

					super().__init__(
					    lowerCamelCase__		,     tokenizer_file=lowerCamelCase__		,     do_lower_case=lowerCamelCase__		,     remove_space=lowerCamelCase__		,     keep_accents=lowerCamelCase__		,     unk_token=lowerCamelCase__		,     sep_token=lowerCamelCase__		,     pad_token=lowerCamelCase__		,     cls_token=lowerCamelCase__		,     mask_token=lowerCamelCase__		,     **lowerCamelCase__		,     )

					_UpperCAmelCase						:				Optional[Any]	    =					do_lower_case
					_UpperCAmelCase						:				Tuple	    =					remove_space
					_UpperCAmelCase						:				List[Any]	    =					keep_accents
					_UpperCAmelCase						:				Tuple	    =					vocab_file
					_UpperCAmelCase						:				str	    =					False if not self.vocab_file else True




			def 							lowerCAmelCase__			(						self     :   str		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Tuple	    =					[self.cls_token_id]
					if token_ids_a is None:
							return cls + token_ids_a + sep
					return cls + token_ids_a + sep + token_ids_a + sep




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   List[int]		,     lowerCamelCase__     :   Optional[List[int]] = None       )      ->List[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					[self.sep_token_id]
					_UpperCAmelCase						:				Optional[Any]	    =					[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep       ) * [0]
					return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]






			def 							lowerCAmelCase__			(						self     :   Dict		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   Optional[str] = None       )      ->Tuple[str]:



					'''simple docstring'''







					if not os.path.isdir(lowerCamelCase__       ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
							return
					_UpperCAmelCase						:				Union[str, Any]	    =					os.path.join(
					    lowerCamelCase__		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

					if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowerCamelCase__       ):
							copyfile(self.vocab_file		,     lowerCamelCase__       )

					return (out_vocab_file,)



