"""simple docstring"""
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings




@maybe_allow_in_graph
class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=0.0    ,			lowercase = None    ,			lowercase = "geglu"    ,			lowercase = None    ,			lowercase = False    ,			lowercase = False    ,			lowercase = False    ,			lowercase = False    ,			lowercase = True    ,			lowercase = "layer_norm"    ,			lowercase = False    ,			)	->			int:





        '''simple docstring'''





        super().__init__()
        a__:     Dict										=	only_cross_attention

        a__:     Tuple										=	(num_embeds_ada_norm is not None) and norm_type == """ada_norm_zero"""
        a__:     Union[str, Any]										=	(num_embeds_ada_norm is not None) and norm_type == """ada_norm"""

        if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
          raise ValueError(
              f'`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to'
              f' define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.')

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        if self.use_ada_layer_norm:
          a__:     Dict										=	AdaLayerNorm(lowerCamelCase__    ,			lowerCamelCase__)
        elif self.use_ada_layer_norm_zero:
          a__:     List[str]										=	AdaLayerNormZero(lowerCamelCase__    ,			lowerCamelCase__)
        else:
          a__:     Tuple										=	nn.LayerNorm(lowerCamelCase__    ,			elementwise_affine=lowerCamelCase__)
        a__:     Any										=	Attention(
            query_dim=lowerCamelCase__    ,			heads=lowerCamelCase__    ,			dim_head=lowerCamelCase__    ,			dropout=lowerCamelCase__    ,			bias=lowerCamelCase__    ,			cross_attention_dim=cross_attention_dim if only_cross_attention else None    ,			upcast_attention=lowerCamelCase__    ,			)

        # 2. Cross-Attn
        if cross_attention_dim is not None or double_self_attention:
          # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
          # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
          # the second cross attention block.
          a__:     int										=	(
              AdaLayerNorm(lowerCamelCase__    ,			lowerCamelCase__)
              if self.use_ada_layer_norm
              else nn.LayerNorm(lowerCamelCase__    ,			elementwise_affine=lowerCamelCase__)
          )
          a__:     Dict										=	Attention(
              query_dim=lowerCamelCase__    ,			cross_attention_dim=cross_attention_dim if not double_self_attention else None    ,			heads=lowerCamelCase__    ,			dim_head=lowerCamelCase__    ,			dropout=lowerCamelCase__    ,			bias=lowerCamelCase__    ,			upcast_attention=lowerCamelCase__    ,			)  # is self-attn if encoder_hidden_states is none
        else:
          a__:     Dict										=	None
          a__:     Dict										=	None

        # 3. Feed-forward
        a__:     Optional[Any]										=	nn.LayerNorm(lowerCamelCase__    ,			elementwise_affine=lowerCamelCase__)
        a__:     int										=	FeedForward(lowerCamelCase__    ,			dropout=lowerCamelCase__    ,			activation_fn=lowerCamelCase__    ,			final_dropout=lowerCamelCase__)

        # let chunk size default to None
        a__:     Optional[Any]										=	None
        a__:     int										=	0



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     List[str]										=	chunk_size
        a__:     int										=	dim



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        if self.use_ada_layer_norm:
          a__:     Optional[int]										=	self.norma(lowerCamelCase__    ,			lowerCamelCase__)
        elif self.use_ada_layer_norm_zero:
          a__:     Tuple										=	self.norma(
              lowerCamelCase__    ,			lowerCamelCase__    ,			lowerCamelCase__    ,			hidden_dtype=hidden_states.dtype)
        else:
          a__:     Tuple										=	self.norma(lowerCamelCase__)

        a__:     Dict										=	cross_attention_kwargs if cross_attention_kwargs is not None else {}

        a__:     str										=	self.attna(
            lowerCamelCase__    ,			encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None    ,			attention_mask=lowerCamelCase__    ,			**lowerCamelCase__    ,			)
        if self.use_ada_layer_norm_zero:
          a__:     List[Any]										=	gate_msa.unsqueeze(1) * attn_output
        a__:     str										=	attn_output + hidden_states

        # 2. Cross-Attention
        if self.attna is not None:
          a__:     Optional[int]										=	(
              self.norma(lowerCamelCase__    ,			lowerCamelCase__) if self.use_ada_layer_norm else self.norma(lowerCamelCase__)
          )

          a__:     Optional[Any]										=	self.attna(
              lowerCamelCase__    ,			encoder_hidden_states=lowerCamelCase__    ,			attention_mask=lowerCamelCase__    ,			**lowerCamelCase__    ,			)
          a__:     Dict										=	attn_output + hidden_states

        # 3. Feed-forward
        a__:     str										=	self.norma(lowerCamelCase__)

        if self.use_ada_layer_norm_zero:
          a__:     Tuple										=	norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

        if self._chunk_size is not None:
          # "feed_forward_chunk_size" can be used to save memory
          if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
            raise ValueError(
                f'`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.')

          a__:     Any										=	norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
          a__:     Optional[Any]										=	torch.cat(
              [self.ff(lowerCamelCase__) for hid_slice in norm_hidden_states.chunk(lowerCamelCase__    ,			dim=self._chunk_dim)]    ,			dim=self._chunk_dim    ,			)
        else:
          a__:     Dict										=	self.ff(lowerCamelCase__)

        if self.use_ada_layer_norm_zero:
          a__:     Optional[Any]										=	gate_mlp.unsqueeze(1) * ff_output

        a__:     Optional[int]										=	ff_output + hidden_states

        return hidden_states







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase = None    ,			lowercase = 4    ,			lowercase = 0.0    ,			lowercase = "geglu"    ,			lowercase = False    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        super().__init__()
        a__:     str										=	int(dim * mult)
        a__:     Optional[Any]										=	dim_out if dim_out is not None else dim

        if activation_fn == "gelu":
          a__:     Dict										=	GELU(lowerCamelCase__    ,			lowerCamelCase__)
        if activation_fn == "gelu-approximate":
          a__:     Optional[int]										=	GELU(lowerCamelCase__    ,			lowerCamelCase__    ,			approximate='tanh')
        elif activation_fn == "geglu":
          a__:     Any										=	GEGLU(lowerCamelCase__    ,			lowerCamelCase__)
        elif activation_fn == "geglu-approximate":
          a__:     Optional[Any]										=	ApproximateGELU(lowerCamelCase__    ,			lowerCamelCase__)

        a__:     Dict										=	nn.ModuleList([])
        # project in
        self.net.append(lowerCamelCase__)
        # project dropout
        self.net.append(nn.Dropout(lowerCamelCase__))
        # project out
        self.net.append(nn.Linear(lowerCamelCase__    ,			lowerCamelCase__))
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
          self.net.append(nn.Dropout(lowerCamelCase__))



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





        '''simple docstring'''





        for module in self.net:
          a__:     int										=	module(lowerCamelCase__)
        return hidden_states







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase = "none")	->			int:





        '''simple docstring'''





        super().__init__()
        a__:     Optional[int]										=	nn.Linear(lowerCamelCase__    ,			lowerCamelCase__)
        a__:     int										=	approximate



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Any:





        '''simple docstring'''





        if gate.device.type != "mps":
          return F.gelu(lowerCamelCase__    ,			approximate=self.approximate)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa)    ,			approximate=self.approximate).to(dtype=gate.dtype)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	self.proj(lowerCamelCase__)
        a__:     Union[str, Any]										=	self.gelu(lowerCamelCase__)
        return hidden_states







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        super().__init__()
        a__:     Union[str, Any]										=	nn.Linear(lowerCamelCase__    ,			dim_out * 2)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if gate.device.type != "mps":
          return F.gelu(lowerCamelCase__)
        # mps: gelu is not implemented for float16
        return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     Any										=	self.proj(lowerCamelCase__).chunk(2    ,			dim=-1)
        return hidden_states * self.gelu(lowerCamelCase__)







class    __snake_case       (     nn.Module				):



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





        '''simple docstring'''





        super().__init__()
        a__:     Union[str, Any]										=	nn.Linear(lowerCamelCase__    ,			lowerCamelCase__)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	self.proj(lowerCamelCase__)
        return x * torch.sigmoid(1.702 * x)







class    __snake_case       (     nn.Module				):



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





        '''simple docstring'''





        super().__init__()
        a__:     Dict										=	nn.Embedding(lowerCamelCase__    ,			lowerCamelCase__)
        a__:     Tuple										=	nn.SiLU()
        a__:     Tuple										=	nn.Linear(lowerCamelCase__    ,			embedding_dim * 2)
        a__:     List[str]										=	nn.LayerNorm(lowerCamelCase__    ,			elementwise_affine=lowerCamelCase__)



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





        '''simple docstring'''





        a__:     int										=	self.linear(self.silu(self.emb(lowerCamelCase__)))
        a__:     Optional[int]										=	torch.chunk(lowerCamelCase__    ,			2)
        a__:     Tuple										=	self.norm(lowerCamelCase__) * (1 + scale) + shift
        return x







class    __snake_case       (     nn.Module				):



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





        '''simple docstring'''





        super().__init__()

        a__:     Union[str, Any]										=	CombinedTimestepLabelEmbeddings(lowerCamelCase__    ,			lowerCamelCase__)

        a__:     List[Any]										=	nn.SiLU()
        a__:     str										=	nn.Linear(lowerCamelCase__    ,			6 * embedding_dim    ,			bias=lowerCamelCase__)
        a__:     List[Any]										=	nn.LayerNorm(lowerCamelCase__    ,			elementwise_affine=lowerCamelCase__    ,			eps=1e-6)



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





        '''simple docstring'''





        a__:     Tuple										=	self.linear(self.silu(self.emb(lowerCamelCase__    ,			lowerCamelCase__    ,			hidden_dtype=lowerCamelCase__)))
        a__:     Optional[Any]										=	emb.chunk(6    ,			dim=1)
        a__:     List[str]										=	self.norm(lowerCamelCase__) * (1 + scale_msa[:, None]) + shift_msa[:, None]
        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp







class    __snake_case       (     nn.Module				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase = None    ,			lowercase = 1e-5)	->			Optional[Any]:





        '''simple docstring'''





        super().__init__()
        a__:     int										=	num_groups
        a__:     Dict										=	eps

        if act_fn is None:
          a__:     Union[str, Any]										=	None
        else:
          a__:     Optional[int]										=	get_activation(lowerCamelCase__)

        a__:     Dict										=	nn.Linear(lowerCamelCase__    ,			out_dim * 2)



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





        '''simple docstring'''





        if self.act:
          a__:     Dict										=	self.act(lowerCamelCase__)
        a__:     Optional[int]										=	self.linear(lowerCamelCase__)
        a__:     int										=	emb[:, :, None, None]
        a__:     Tuple										=	emb.chunk(2    ,			dim=1)

        a__:     Any										=	F.group_norm(lowerCamelCase__    ,			self.num_groups    ,			eps=self.eps)
        a__:     List[str]										=	x * (1 + scale) + shift
        return x







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__		)		)


