






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


			def __init__(      self     :  List[Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  List[str]   ,       snake_case__     :  Dict   ,       snake_case__     :  int = 1.0   ,       snake_case__     :  Optional[int] = None   ,       ):
										super().__init__()
										lowerCamelCase_       :							Union[str, Any]									=initial_learning_rate
										lowerCamelCase_       :							List[Any]									=warmup_steps
										lowerCamelCase_       :							int									=power
										lowerCamelCase_       :							Optional[int]									=decay_schedule_fn
										lowerCamelCase_       :							int									=name


			def __call__(      self     :  int   ,       snake_case__     :  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`.
																	lowerCamelCase_       :							str									=tf.cast(lowerCamelCase__   ,       tf.floataa					)
																	lowerCamelCase_       :							List[Any]									=tf.cast(self.warmup_steps   ,       tf.floataa					)
																	lowerCamelCase_       :							Dict									=global_step_float / warmup_steps_float
																	lowerCamelCase_       :							Union[str, 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 	UpperCAmelCase__						(      self     :  int					):
										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  _snake_case				(     lowerCamelCase__	:    Any				,						lowerCamelCase__	:    List[str]				,						lowerCamelCase__	:    str				,						lowerCamelCase__	:    Any = 0.0				,						lowerCamelCase__	:    List[Any] = 0.9				,						lowerCamelCase__	:    str = 0.999				,						lowerCamelCase__	:    Any = 1e-8				,						lowerCamelCase__	:    int = None				,						lowerCamelCase__	:    List[str] = None				,						lowerCamelCase__	:    Optional[Any] = 0.0				,						lowerCamelCase__	:    Dict = 1.0				,						lowerCamelCase__	:    List[Any] = None				,						)				->	Union[str, Any]:
							lowerCamelCase_       :							Optional[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:
														lowerCamelCase_       :							Dict									=WarmUp(
														    initial_learning_rate=_lowerCAmelCase				,						decay_schedule_fn=_lowerCAmelCase				,						warmup_steps=_lowerCAmelCase				,						)
							if weight_decay_rate > 0.0:
														lowerCamelCase_       :							str									=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:
														lowerCamelCase_       :							Optional[int]									=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    lowercase__			(							SCREAMING_SNAKE_CASE__   ):


			def __init__(      self     :  Optional[Any]   ,       snake_case__     :  Tuple = 0.001   ,       snake_case__     :  Dict = 0.9   ,       snake_case__     :  Union[str, Any] = 0.999   ,       snake_case__     :  str = 1E-7   ,       snake_case__     :  Dict = False   ,       snake_case__     :  int = 0.0   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  Union[str, Any] = "AdamWeightDecay"   ,       **snake_case__     :  Any   ,       ):
										super().__init__(lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__   ,       **lowerCamelCase__					)
										lowerCamelCase_       :							int									=weight_decay_rate
										lowerCamelCase_       :							Any									=include_in_weight_decay
										lowerCamelCase_       :							Dict									=exclude_from_weight_decay


			@classmethod
			def 	UpperCAmelCase__						(      cls     :  Tuple   ,       snake_case__     :  Optional[int]					):
										lowerCamelCase_       :							Tuple									={"""WarmUp""": WarmUp}
										return super(lowerCamelCase__   ,       cls					).from_config(lowerCamelCase__   ,       custom_objects=lowerCamelCase__					)


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  Any					):
										super(lowerCamelCase__   ,       self					)._prepare_local(lowerCamelCase__   ,       lowerCamelCase__   ,       lowerCamelCase__					)
										lowerCamelCase_       :							List[str]									=tf.constant(
										    self.weight_decay_rate   ,       name="adam_weight_decay_rate"					)


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


			def 	UpperCAmelCase__						(      self     :  Dict   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Tuple   ,       snake_case__     :  List[str]					):
										if apply_state is None:
																	return self._decayed_lr_t[var_dtype], {}

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

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


			def 	UpperCAmelCase__						(      self     :  List[Any]   ,       snake_case__     :  List[Any]   ,       snake_case__     :  Dict   ,       snake_case__     :  Union[str, Any]=None					):
										lowerCamelCase_       :							str									=self._get_lr(var.device   ,       var.dtype.base_dtype   ,       lowerCamelCase__					)
										lowerCamelCase_       :							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     :  List[Any]   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  str   ,       snake_case__     :  Dict   ,       snake_case__     :  List[Any]=None					):
										lowerCamelCase_       :							Tuple									=self._get_lr(var.device   ,       var.dtype.base_dtype   ,       lowerCamelCase__					)
										lowerCamelCase_       :							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 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_       :							Dict									=super().get_config()
										config.update({"weight_decay_rate": self.weight_decay_rate}					)
										return config


			def 	UpperCAmelCase__						(      self     :  int   ,       snake_case__     :  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    lowercase__			(							SCREAMING_SNAKE_CASE__   ):


			def __init__(      self     :  List[str]					):
										lowerCamelCase_       :							List[str]									=[]
										lowerCamelCase_       :							List[str]									=None


			@property
			def 	UpperCAmelCase__						(      self     :  List[str]					):
										if self._accum_steps is None:
																	lowerCamelCase_       :							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 	UpperCAmelCase__						(      self     :  List[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     :  str   ,       snake_case__     :  Optional[Any]					):
										if not self._gradients:
																	lowerCamelCase_       :							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     :  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__					)					)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


