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  ):


				'''simple docstring'''






				def __init__(						self		,	lowercase		,	lowercase		,	lowercase		,	lowercase = 1.0		,	lowercase = None		,	):



						"""simple docstring"""





						super().__init__()
						A_		:				Any     =      initial_learning_rate
						A_		:				Optional[Any]     =      warmup_steps
						A_		:				int     =      power
						A_		:				Optional[Any]     =      decay_schedule_fn
						A_		:				Any     =      name
				def __call__(						self		,	lowercase					):



						"""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_		:				int     =      tf.cast(_UpperCamelCase		,	tf.floataa					)
								A_		:				Optional[Any]     =      tf.cast(self.warmup_steps		,	tf.floataa					)
								A_		:				Tuple     =      global_step_float / warmup_steps_float
								A_		:				Dict     =      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   lowerCAmelCase_   (						self					):



						"""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 				UpperCamelCase						(       __lowercase						: float						,__lowercase						: int						,__lowercase						: int						,__lowercase						: float = 0.0						,__lowercase						: float = 0.9						,__lowercase						: float = 0.9_99						,__lowercase						: float = 1e-8						,__lowercase						: Optional[float] = None						,__lowercase						: Optional[float] = None						,__lowercase						: float = 0.0						,__lowercase						: float = 1.0						,__lowercase						: Optional[List[str]] = None						,):
		'''simple docstring'''




		A_		:				List[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:
				A_		:				List[str]     =      WarmUp(
				    initial_learning_rate=__lowerCAmelCase						,decay_schedule_fn=__lowerCAmelCase						,warmup_steps=__lowerCAmelCase						,)
		if weight_decay_rate > 0.0:
				A_		:				Tuple     =      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_		:				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 UpperCAmelCase						(					__a  ):


				'''simple docstring'''






				def __init__(						self		,	lowercase = 0.001		,	lowercase = 0.9		,	lowercase = 0.999		,	lowercase = 1E-7		,	lowercase = False		,	lowercase = 0.0		,	lowercase = None		,	lowercase = None		,	lowercase = "AdamWeightDecay"		,	**lowercase		,	):



						"""simple docstring"""





						super().__init__(_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase		,	**_UpperCamelCase					)
						A_		:				Dict     =      weight_decay_rate
						A_		:				Union[str, Any]     =      include_in_weight_decay
						A_		:				Optional[int]     =      exclude_from_weight_decay
				@classmethod
				def   lowerCAmelCase_   (						cls		,	lowercase					):



						"""simple docstring"""





						A_		:				List[Any]     =      {'WarmUp': WarmUp}
						return super(_UpperCamelCase		,	cls					).from_config(_UpperCamelCase		,	custom_objects=_UpperCamelCase					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase					):



						"""simple docstring"""





						super(_UpperCamelCase		,	self					)._prepare_local(_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase					)
						A_		:				Union[str, Any]     =      tf.constant(
						    self.weight_decay_rate		,	name='adam_weight_decay_rate'					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase					):



						"""simple docstring"""





						A_		:				int     =      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		,	lowercase		,	lowercase=None		,	**lowercase					):



						"""simple docstring"""





						A_   ,			A_		:				str     =      list(zip(*_UpperCamelCase					)					)
						return super(_UpperCamelCase		,	self					).apply_gradients(zip(_UpperCamelCase		,	_UpperCamelCase					)		,	name=_UpperCamelCase		,	**_UpperCamelCase					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase					):



						"""simple docstring"""





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

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

						return coefficients["lr_t"], {"apply_state": apply_state}
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase=None					):



						"""simple docstring"""





						A_   ,			A_		:				Tuple     =      self._get_lr(var.device		,	var.dtype.base_dtype		,	_UpperCamelCase					)
						A_		:				Union[str, Any]     =      self._decay_weights_op(_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase					)
						with tf.control_dependencies([decay]					):
								return super(_UpperCamelCase		,	self					)._resource_apply_dense(_UpperCamelCase		,	_UpperCamelCase		,	**_UpperCamelCase					)
				def   lowerCAmelCase_   (						self		,	lowercase		,	lowercase		,	lowercase		,	lowercase=None					):



						"""simple docstring"""





						A_   ,			A_		:				List[str]     =      self._get_lr(var.device		,	var.dtype.base_dtype		,	_UpperCamelCase					)
						A_		:				Optional[Any]     =      self._decay_weights_op(_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase					)
						with tf.control_dependencies([decay]					):
								return super(_UpperCamelCase		,	self					)._resource_apply_sparse(_UpperCamelCase		,	_UpperCamelCase		,	_UpperCamelCase		,	**_UpperCamelCase					)
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						A_		:				Optional[Any]     =      super().get_config()
						config.update({'weight_decay_rate': self.weight_decay_rate}					)
						return config







				def   lowerCAmelCase_   (						self		,	lowercase					):



						"""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(_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 UpperCAmelCase						(					__a  ):


				'''simple docstring'''






				def __init__(						self					):



						"""simple docstring"""





						A_		:				Optional[int]     =      []
						A_		:				Dict     =      None
				@property
				def   lowerCAmelCase_   (						self					):



						"""simple docstring"""





						if self._accum_steps is None:
								A_		:				Optional[int]     =      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   lowerCAmelCase_   (						self					):



						"""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		,	lowercase					):



						"""simple docstring"""





						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   lowerCAmelCase_   (						self					):



						"""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(_UpperCamelCase					)					)








from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A   =     logging.get_logger(__name__)


_A   =     TypeVar("DatasetType", Dataset, IterableDataset)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[List[float]] = None    ,							__lowerCAmelCase  :  Optional[int] = None    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,							):




			"""simple docstring"""


			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""				)
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)
			else:
						return _interleave_iterable_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  int = 0    ,							):




			"""simple docstring"""


			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)
			else:
						return _concatenate_iterable_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)



