


'''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   ,					lowerCAmelCase    :				float   ,					lowerCAmelCase    :				Callable   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				float = 1.0   ,					lowerCAmelCase    :				str = None   ,					)->  int:

										"""simple docstring"""




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




				def __call__(	self    :				Optional[Any]   ,					lowerCAmelCase    :				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`.
																UpperCAmelCase         =			tf.cast(lowerCAmelCase   ,					tf.floataa   )
																UpperCAmelCase         =			tf.cast(self.warmup_steps   ,					tf.floataa   )
																UpperCAmelCase         =			global_step_float / warmup_steps_float
																UpperCAmelCase         =			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   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__   ( A     :    Optional[Any]   ,     A     :    List[str]   ,     A     :    Tuple   ,     A     :    str = 0.0   ,     A     :    Optional[int] = 0.9   ,     A     :    Union[str, Any] = 0.999   ,     A     :    List[Any] = 1E-8   ,     A     :    str = None   ,     A     :    Optional[int] = None   ,     A     :    Tuple = 0.0   ,     A     :    Any = 1.0   ,     A     :    List[str] = None   ,     ):




						'''simple docstring'''






						UpperCAmelCase         =			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         =			WarmUp(
												    initial_learning_rate=lowerCamelCase__   ,     decay_schedule_fn=lowerCamelCase__   ,     warmup_steps=lowerCamelCase__   ,     )
						if weight_decay_rate > 0.0:
												UpperCAmelCase         =			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         =			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__(     lowerCAmelCase ):




				def __init__(	self    :				Optional[int]   ,					lowerCAmelCase    :				Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001   ,					lowerCAmelCase    :				float = 0.9   ,					lowerCAmelCase    :				float = 0.999   ,					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    :				Union[str, Any]   ,					)->  Union[str, Any]:

										"""simple docstring"""




										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   ,					**lowerCAmelCase   )
										UpperCAmelCase         =			weight_decay_rate
										UpperCAmelCase         =			include_in_weight_decay
										UpperCAmelCase         =			exclude_from_weight_decay




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

										"""simple docstring"""




										UpperCAmelCase         =			{'''WarmUp''': WarmUp}
										return super(lowerCAmelCase   ,					cls   ).from_config(lowerCAmelCase   ,					custom_objects=lowerCAmelCase   )




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

										"""simple docstring"""




										super(lowerCAmelCase   ,					self   )._prepare_local(lowerCAmelCase   ,					lowerCAmelCase   ,					lowerCAmelCase   )
										UpperCAmelCase         =			tf.constant(
										    self.weight_decay_rate   ,					name='''adam_weight_decay_rate'''   )




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				Optional[int]   ,					lowerCAmelCase    :				int   ,					lowerCAmelCase    :				Dict   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			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]   ,					lowerCAmelCase    :				Optional[Any]   ,					lowerCAmelCase    :				Dict=None   ,					**lowerCAmelCase    :				str   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			list(zip(*lowerCAmelCase   )   )
										return super(lowerCAmelCase   ,					self   ).apply_gradients(zip(lowerCAmelCase   ,					lowerCAmelCase   )   ,					name=lowerCAmelCase   ,					**lowerCAmelCase   )




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

										"""simple docstring"""




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

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

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




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

										"""simple docstring"""




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




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

										"""simple docstring"""




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




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

										"""simple docstring"""




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





				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				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(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  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				Any   )->  int:

										"""simple docstring"""




										UpperCAmelCase         =			[]
										UpperCAmelCase         =			None




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

										"""simple docstring"""




										if self._accum_steps is None:
																UpperCAmelCase         =			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   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   ,					lowerCAmelCase    :				List[str]   )->  Dict:

										"""simple docstring"""




										if not self._gradients:
																UpperCAmelCase         =			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   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(lowerCAmelCase   )   )









'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						return DownloadCommand(args.model   ,     args.cache_dir   ,     args.force   ,     args.trust_remote_code  )






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser('''download'''   )
										download_parser.add_argument(
										    '''--cache-dir'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Path to location to store the models'''   )
										download_parser.add_argument(
										    '''--force'''   ,					action='''store_true'''   ,					help='''Force the model to be download even if already in cache-dir'''   )
										download_parser.add_argument(
										    '''--trust-remote-code'''   ,					action='''store_true'''   ,					help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,					)
										download_parser.add_argument('''model'''   ,					type=lowerCAmelCase   ,					help='''Name of the model to download'''   )
										download_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				bool   ,					lowerCAmelCase    :				bool   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			model
										UpperCAmelCase         =			cache
										UpperCAmelCase         =			force
										UpperCAmelCase         =			trust_remote_code





				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )
										AutoTokenizer.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )





