"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple

import numpy as np

from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy


if is_flax_available():
 import jax.numpy as jnp




class    __snake_case       (     SCREAMING_SNAKE_CASE__				):



      def __get__(						self    ,			lowercase    ,			lowercase=None)	->			str:





        '''simple docstring'''





        if obj is None:
          return self
        if self.fget is None:
          raise AttributeError('unreadable attribute')
        a__:     Union[str, Any]										=	"""__cached_""" + self.fget.__name__
        a__:     Optional[int]										=	getattr(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)
        if cached is None:
          a__:     Tuple										=	self.fget(lowerCamelCase__)
          setattr(lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__)
        return cached







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Dict:
  a__:     Any										=	val.lower()
  if val in {"y", "yes", "t", "true", "on", "1"}:
    return 1
  if val in {"n", "no", "f", "false", "off", "0"}:
    return 0
  raise ValueError(F'invalid truth value {val!r}'   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[Any]:






  if is_torch_fx_proxy(_lowerCAmelCase   ):
    return True
  if is_torch_available():
    import torch

    if isinstance(_lowerCAmelCase		,     torch.Tensor   ):
      return True
  if is_tf_available():
    import tensorflow as tf

    if isinstance(_lowerCAmelCase		,     tf.Tensor   ):
      return True

  if is_flax_available():
    import jax.numpy as jnp
    from jax.core import Tracer

    if isinstance(_lowerCAmelCase		,     (jnp.ndarray, Tracer)   ):
      return True

  return isinstance(_lowerCAmelCase		,     np.ndarray   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[str]:
  return isinstance(_lowerCAmelCase		,     np.ndarray   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  return _is_numpy(_lowerCAmelCase   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->str:
  import torch

  return isinstance(_lowerCAmelCase		,     torch.Tensor   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  return False if not is_torch_available() else _is_torch(_lowerCAmelCase   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  import torch

  return isinstance(_lowerCAmelCase		,     torch.device   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Tuple:
  return False if not is_torch_available() else _is_torch_device(_lowerCAmelCase   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[str]:
  import torch

  if isinstance(_lowerCAmelCase		,     _lowerCAmelCase   ):
    if hasattr(_lowerCAmelCase		,     _lowerCAmelCase   ):
      a__:     Tuple										=	getattr(_lowerCAmelCase		,     _lowerCAmelCase   )
    else:
      return False
  return isinstance(_lowerCAmelCase		,     torch.dtype   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[str]:
  return False if not is_torch_available() else _is_torch_dtype(_lowerCAmelCase   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  import tensorflow as tf

  return isinstance(_lowerCAmelCase		,     tf.Tensor   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  return False if not is_tf_available() else _is_tensorflow(_lowerCAmelCase   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[str]:
  import tensorflow as tf

  # the `is_symbolic_tensor` predicate is only available starting with TF 2.14
  if hasattr(_lowerCAmelCase		,     'is_symbolic_tensor'   ):
    return tf.is_symbolic_tensor(_lowerCAmelCase   )
  return type(_lowerCAmelCase   ) == tf.Tensor







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->int:
  return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCAmelCase   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->str:
  import jax.numpy as jnp  # noqa: F811

  return isinstance(_lowerCAmelCase		,     jnp.ndarray   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[int]:
  return False if not is_flax_available() else _is_jax(_lowerCAmelCase   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[Any]:






  if isinstance(_lowerCAmelCase		,     (dict, UserDict)   ):
    return {k: to_py_obj(_lowerCAmelCase   ) for k, v in obj.items()}
  elif isinstance(_lowerCAmelCase		,     (list, tuple)   ):
    return [to_py_obj(_lowerCAmelCase   ) for o in obj]
  elif is_tf_tensor(_lowerCAmelCase   ):
    return obj.numpy().tolist()
  elif is_torch_tensor(_lowerCAmelCase   ):
    return obj.detach().cpu().tolist()
  elif is_jax_tensor(_lowerCAmelCase   ):
    return np.asarray(_lowerCAmelCase   ).tolist()
  elif isinstance(_lowerCAmelCase		,     (np.ndarray, np.number)   ):  # tolist also works on 0d np arrays
    return obj.tolist()
  else:
    return obj







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->str:






  if isinstance(_lowerCAmelCase		,     (dict, UserDict)   ):
    return {k: to_numpy(_lowerCAmelCase   ) for k, v in obj.items()}
  elif isinstance(_lowerCAmelCase		,     (list, tuple)   ):
    return np.array(_lowerCAmelCase   )
  elif is_tf_tensor(_lowerCAmelCase   ):
    return obj.numpy()
  elif is_torch_tensor(_lowerCAmelCase   ):
    return obj.detach().cpu().numpy()
  elif is_jax_tensor(_lowerCAmelCase   ):
    return np.asarray(_lowerCAmelCase   )
  else:
    return obj




class    __snake_case       (     SCREAMING_SNAKE_CASE__				):



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Tuple										=	fields(self)

        # Safety and consistency checks
        if not len(lowerCamelCase__):
          raise ValueError(f'{self.__class__.__name__} has no fields.')
        if not all(field.default is None for field in class_fields[1:]):
          raise ValueError(f'{self.__class__.__name__} should not have more than one required field.')

        a__:     Dict										=	getattr(self    ,			class_fields[0].name)
        a__:     Optional[int]										=	all(getattr(self    ,			field.name) is None for field in class_fields[1:])

        if other_fields_are_none and not is_tensor(lowerCamelCase__):
          if isinstance(lowerCamelCase__    ,			lowerCamelCase__):
            a__:     List[Any]										=	first_field.items()
            a__:     str										=	True
          else:
            try:
              a__:     str										=	iter(lowerCamelCase__)
              a__:     Union[str, Any]										=	True
            except TypeError:
              a__:     Optional[int]										=	False

            # if we provided an iterator as first field and the iterator is a (key, value) iterator
            # set the associated fields
          if first_field_iterator:
            for idx, element in enumerate(lowerCamelCase__):
              if (
                  not isinstance(lowerCamelCase__    ,			(list, tuple))
                  or not len(lowerCamelCase__) == 2
                  or not isinstance(element[0]    ,			lowerCamelCase__)
              ):
                if idx == 0:
                  # If we do not have an iterator of key/values, set it as attribute
                  a__:     Dict										=	first_field
                else:
                  # If we have a mixed iterator, raise an error
                  raise ValueError(
                      f'Cannot set key/value for {element}. It needs to be a tuple (key, value).')
                break
              setattr(self    ,			element[0]    ,			element[1])
              if element[1] is not None:
                a__:     Optional[int]										=	element[1]
          elif first_field is not None:
            a__:     Union[str, Any]										=	first_field
        else:
          for field in class_fields:
            a__:     Tuple										=	getattr(self    ,			field.name)
            if v is not None:
              a__:     int										=	v



      def __delitem__(						self    ,			*lowercase    ,			**lowercase)	->			List[Any]:





        '''simple docstring'''





        raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.')



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			Optional[int]:





        '''simple docstring'''





        raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.')



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.')



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			List[str]:





        '''simple docstring'''





        raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.')



      def __getitem__(						self    ,			lowercase)	->			str:





        '''simple docstring'''





        if isinstance(lowerCamelCase__    ,			lowerCamelCase__):
          a__:     str										=	dict(self.items())
          return inner_dict[k]
        else:
          return self.to_tuple()[k]



      def __setattr__(						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        if name in self.keys() and value is not None:
          # Don't call self.__setitem__ to avoid recursion errors
          super().__setitem__(lowerCamelCase__    ,			lowerCamelCase__)
        super().__setattr__(lowerCamelCase__    ,			lowerCamelCase__)



      def __setitem__(						self    ,			lowercase    ,			lowercase)	->			str:





        '''simple docstring'''





        super().__setitem__(lowerCamelCase__    ,			lowerCamelCase__)
        # Don't call self.__setattr__ to avoid recursion errors
        super().__setattr__(lowerCamelCase__    ,			lowerCamelCase__)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return tuple(self[k] for k in self.keys())







class    __snake_case       (     SCREAMING_SNAKE_CASE__       ,      SCREAMING_SNAKE_CASE__				):



      @classmethod
      def   lowerCamelCase_   (						cls    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        raise ValueError(
            f'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}')







class    __snake_case       (     SCREAMING_SNAKE_CASE__				):
      a__        =		"longest"
      a__        =		"max_length"
      a__        =		"do_not_pad"







class    __snake_case       (     SCREAMING_SNAKE_CASE__				):
      a__        =		"pt"
      a__        =		"tf"
      a__        =		"np"
      a__        =		"jax"







class    __snake_case       :



      def __init__(						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Dict										=	context_managers
        a__:     List[str]										=	ExitStack()



      def __enter__(						self)	->			Tuple:





        '''simple docstring'''





        for context_manager in self.context_managers:
          self.stack.enter_context(lowerCamelCase__)



      def __exit__(						self    ,			*lowercase    ,			**lowercase)	->			Dict:





        '''simple docstring'''





        self.stack.__exit__(*lowerCamelCase__    ,			**lowerCamelCase__)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[Any]:
  a__:     int										=	infer_framework(_lowerCAmelCase   )
  if framework == "tf":
    a__:     Optional[int]										=	inspect.signature(model_class.call   )  # TensorFlow models
  elif framework == "pt":
    a__:     Any										=	inspect.signature(model_class.forward   )  # PyTorch models
  else:
    a__:     List[Any]										=	inspect.signature(model_class.__call__   )  # Flax models

  for p in signature.parameters:
    if p == "return_loss" and signature.parameters[p].default is True:
      return True

  return False







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Optional[int]:
  a__:     Dict										=	model_class.__name__
  a__:     List[Any]										=	infer_framework(_lowerCAmelCase   )
  if framework == "tf":
    a__:     Any										=	inspect.signature(model_class.call   )  # TensorFlow models
  elif framework == "pt":
    a__:     Union[str, Any]										=	inspect.signature(model_class.forward   )  # PyTorch models
  else:
    a__:     int										=	inspect.signature(model_class.__call__   )  # Flax models

  if "QuestionAnswering" in model_name:
    return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
  else:
    return [p for p in signature.parameters if "label" in p]







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = ""		,     _SCREAMING_SNAKE_CASE = "."   )	->Dict:






  def _flatten_dict(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=""		,     _SCREAMING_SNAKE_CASE="."   ):
    for k, v in d.items():
      a__:     Any										=	str(_lowerCAmelCase   ) + delimiter + str(_lowerCAmelCase   ) if parent_key else k
      if v and isinstance(_lowerCAmelCase		,     _lowerCAmelCase   ):
        yield from flatten_dict(_lowerCAmelCase		,     _lowerCAmelCase		,     delimiter=_lowerCAmelCase   ).items()
      else:
        yield key, v

  return dict(_flatten_dict(_lowerCAmelCase		,     _lowerCAmelCase		,     _lowerCAmelCase   )   )







@contextmanager
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = False   )	->Any:






  if use_temp_dir:
    with tempfile.TemporaryDirectory() as tmp_dir:
      yield tmp_dir
  else:
    yield working_dir







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->List[Any]:






  if is_numpy_array(_lowerCAmelCase   ):
    return np.transpose(_lowerCAmelCase		,     axes=_lowerCAmelCase   )
  elif is_torch_tensor(_lowerCAmelCase   ):
    return array.T if axes is None else array.permute(*_lowerCAmelCase   )
  elif is_tf_tensor(_lowerCAmelCase   ):
    import tensorflow as tf

    return tf.transpose(_lowerCAmelCase		,     perm=_lowerCAmelCase   )
  elif is_jax_tensor(_lowerCAmelCase   ):
    return jnp.transpose(_lowerCAmelCase		,     axes=_lowerCAmelCase   )
  else:
    raise ValueError(F'Type not supported for transpose: {type(_lowerCAmelCase   )}.'   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:






  if is_numpy_array(_lowerCAmelCase   ):
    return np.reshape(_lowerCAmelCase		,     _lowerCAmelCase   )
  elif is_torch_tensor(_lowerCAmelCase   ):
    return array.reshape(*_lowerCAmelCase   )
  elif is_tf_tensor(_lowerCAmelCase   ):
    import tensorflow as tf

    return tf.reshape(_lowerCAmelCase		,     _lowerCAmelCase   )
  elif is_jax_tensor(_lowerCAmelCase   ):
    return jnp.reshape(_lowerCAmelCase		,     _lowerCAmelCase   )
  else:
    raise ValueError(F'Type not supported for reshape: {type(_lowerCAmelCase   )}.'   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=None   )	->str:






  if is_numpy_array(_lowerCAmelCase   ):
    return np.squeeze(_lowerCAmelCase		,     axis=_lowerCAmelCase   )
  elif is_torch_tensor(_lowerCAmelCase   ):
    return array.squeeze() if axis is None else array.squeeze(dim=_lowerCAmelCase   )
  elif is_tf_tensor(_lowerCAmelCase   ):
    import tensorflow as tf

    return tf.squeeze(_lowerCAmelCase		,     axis=_lowerCAmelCase   )
  elif is_jax_tensor(_lowerCAmelCase   ):
    return jnp.squeeze(_lowerCAmelCase		,     axis=_lowerCAmelCase   )
  else:
    raise ValueError(F'Type not supported for squeeze: {type(_lowerCAmelCase   )}.'   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:






  if is_numpy_array(_lowerCAmelCase   ):
    return np.expand_dims(_lowerCAmelCase		,     _lowerCAmelCase   )
  elif is_torch_tensor(_lowerCAmelCase   ):
    return array.unsqueeze(dim=_lowerCAmelCase   )
  elif is_tf_tensor(_lowerCAmelCase   ):
    import tensorflow as tf

    return tf.expand_dims(_lowerCAmelCase		,     axis=_lowerCAmelCase   )
  elif is_jax_tensor(_lowerCAmelCase   ):
    return jnp.expand_dims(_lowerCAmelCase		,     axis=_lowerCAmelCase   )
  else:
    raise ValueError(F'Type not supported for expand_dims: {type(_lowerCAmelCase   )}.'   )







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->List[str]:






  if is_numpy_array(_lowerCAmelCase   ):
    return np.size(_lowerCAmelCase   )
  elif is_torch_tensor(_lowerCAmelCase   ):
    return array.numel()
  elif is_tf_tensor(_lowerCAmelCase   ):
    import tensorflow as tf

    return tf.size(_lowerCAmelCase   )
  elif is_jax_tensor(_lowerCAmelCase   ):
    return array.size
  else:
    raise ValueError(F'Type not supported for expand_dims: {type(_lowerCAmelCase   )}.'   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Optional[Any]:






  for key, value in auto_map.items():
    if isinstance(_lowerCAmelCase		,     (tuple, list)   ):
      a__:     Tuple										=	[F'{repo_id}--{v}' if (v is not None and """--""" not in v) else v for v in value]
    elif value is not None and "--" not in value:
      a__:     int										=	F'{repo_id}--{value}'

  return auto_map







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Union[str, Any]:






  for base_class in inspect.getmro(_lowerCAmelCase   ):
    a__:     Dict										=	base_class.__module__
    a__:     Optional[Any]										=	base_class.__name__
    if module.startswith('tensorflow'   ) or module.startswith('keras'   ) or name == "TFPreTrainedModel":
      return "tf"
    elif module.startswith('torch'   ) or name == "PreTrainedModel":
      return "pt"
    elif module.startswith('flax'   ) or module.startswith('jax'   ) or name == "FlaxPreTrainedModel":
      return "flax"
  else:
    raise TypeError(F'Could not infer framework from class {model_class}.'   )







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 SCREAMING_SNAKE_CASE__     (		tf.keras.optimizers.schedules.LearningRateSchedule    ):




    '''simple docstring'''






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






    def __call__(       self,		lowerCamelCase__		):
        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 :       str          =   tf.cast(lowerCamelCase__,		tf.floataa		)
            A :       List[Any]          =   tf.cast(self.warmup_steps,		tf.floataa		)
            A :       Dict          =   global_step_float / warmup_steps_float
            A :       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 			_lowerCAmelCase					(       self		):
        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	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 0.9      ,     _lowerCAmelCase = 0.999      ,     _lowerCAmelCase = 1e-8      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 1.0      ,     _lowerCAmelCase = None      ,     )       ->  Union[str, Any]:



    """simple docstring"""






    A :       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:
        A :       Dict          =   WarmUp(
            initial_learning_rate=_lowerCAmelCase      ,     decay_schedule_fn=_lowerCAmelCase      ,     warmup_steps=_lowerCAmelCase      ,     )
    if weight_decay_rate > 0.0:
        A :       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:
        A :       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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 0.001,		lowerCamelCase__ = 0.9,		lowerCamelCase__ = 0.999,		lowerCamelCase__ = 1e-7,		lowerCamelCase__ = False,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = "AdamWeightDecay",		**lowerCamelCase__,		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)
        A :       int          =   weight_decay_rate
        A :       Any          =   include_in_weight_decay
        A :       Dict          =   exclude_from_weight_decay






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__		):
        A :       Tuple          =   {"""WarmUp""": WarmUp}
        return super(lowerCamelCase__,		cls		).from_config(lowerCamelCase__,		custom_objects=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        super(lowerCamelCase__,		self		)._prepare_local(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A :       List[str]          =   tf.constant(
            self.weight_decay_rate,		name="""adam_weight_decay_rate"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       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 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A     ,							A :       Dict          =   list(zip(*lowerCamelCase__		)		)
        return super(lowerCamelCase__,		self		).apply_gradients(zip(lowerCamelCase__,		lowerCamelCase__		),		name=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

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

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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       str          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       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,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       Tuple          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       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 			_lowerCAmelCase					(       self		):
        A :       Dict          =   super().get_config()
        config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
        return config






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






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






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._accum_steps is None:
            A :       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		):
        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,		lowerCamelCase__		):
        if not self._gradients:
            A :       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 			_lowerCAmelCase					(       self		):
        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__		)		)


