




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_lowerCamelCase     					=		logging.get_logger(__name__)  # pylint: disable=invalid-name



class      a	(  _A     ):

     '''simple docstring'''





     def __init__(      self  :     Any   ,       __snake_case  :     WhisperForConditionalGeneration   ,       __snake_case  :     WhisperProcessor   ,       __snake_case  :     AutoencoderKL   ,       __snake_case  :     CLIPTextModel   ,       __snake_case  :     CLIPTokenizer   ,       __snake_case  :     UNetaDConditionModel   ,       __snake_case  :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]   ,       __snake_case  :     StableDiffusionSafetyChecker   ,       __snake_case  :     CLIPImageProcessor   ,       ):
         super().__init__()

         if safety_checker is None:
             logger.warning(
                 F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
                 ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
                 ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
                 ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
                 ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
                 ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''							)

         self.register_modules(
             speech_model=_lowerCAmelCase   ,       speech_processor=_lowerCAmelCase   ,       vae=_lowerCAmelCase   ,       text_encoder=_lowerCAmelCase   ,       tokenizer=_lowerCAmelCase   ,       unet=_lowerCAmelCase   ,       scheduler=_lowerCAmelCase   ,       feature_extractor=_lowerCAmelCase   ,       )
     def     lowerCamelCase_  (      self  :     List[Any]   ,       __snake_case  :     Optional[Union[str, int]] = "auto"							):
         if slice_size == "auto":
             UpperCAmelCase_									=	self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(_lowerCAmelCase							)
     def     lowerCamelCase_  (      self  :     Optional[int]							):
         self.enable_attention_slicing(_lowerCAmelCase							)




     @torch.no_grad()
     def __call__(      self  :     Optional[int]   ,       __snake_case  :     Tuple   ,       __snake_case  :     str=1_60_00   ,       __snake_case  :     int = 5_12   ,       __snake_case  :     int = 5_12   ,       __snake_case  :     int = 50   ,       __snake_case  :     float = 7.5   ,       __snake_case  :     Optional[Union[str, List[str]]] = None   ,       __snake_case  :     Optional[int] = 1   ,       __snake_case  :     float = 0.0   ,       __snake_case  :     Optional[torch.Generator] = None   ,       __snake_case  :     Optional[torch.FloatTensor] = None   ,       __snake_case  :     Optional[str] = "pil"   ,       __snake_case  :     bool = True   ,       __snake_case  :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,       __snake_case  :     int = 1   ,       **__snake_case  :     int   ,       ):
         UpperCAmelCase_									=	self.speech_processor.feature_extractor(
             _lowerCAmelCase   ,       return_tensors='''pt'''   ,       sampling_rate=_lowerCAmelCase							).input_features.to(self.device							)
         UpperCAmelCase_									=	self.speech_model.generate(_lowerCAmelCase   ,       max_length=48_00_00							)

         UpperCAmelCase_									=	self.speech_processor.tokenizer.batch_decode(_lowerCAmelCase   ,       skip_special_tokens=_lowerCAmelCase   ,       normalize=_lowerCAmelCase							)[
             0
         ]

         if isinstance(_lowerCAmelCase   ,       _lowerCAmelCase							):
             UpperCAmelCase_									=	1
         elif isinstance(_lowerCAmelCase   ,       _lowerCAmelCase							):
             UpperCAmelCase_									=	len(_lowerCAmelCase							)
         else:
             raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(_lowerCAmelCase							)}'							)

         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'							)

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(_lowerCAmelCase   ,       _lowerCAmelCase							) or callback_steps <= 0)
         ):
             raise ValueError(
                 F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                 F' {type(_lowerCAmelCase							)}.'							)

         # get prompt text embeddings
         UpperCAmelCase_									=	self.tokenizer(
             _lowerCAmelCase   ,       padding='''max_length'''   ,       max_length=self.tokenizer.model_max_length   ,       return_tensors='''pt'''   ,       )
         UpperCAmelCase_									=	text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             UpperCAmelCase_									=	self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
             logger.warning(
                 '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                 F' {self.tokenizer.model_max_length} tokens: {removed_text}'							)
             UpperCAmelCase_									=	text_input_ids[:, : self.tokenizer.model_max_length]
         UpperCAmelCase_									=	self.text_encoder(text_input_ids.to(self.device							)							)[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_									=	text_embeddings.shape
         UpperCAmelCase_									=	text_embeddings.repeat(1   ,       _lowerCAmelCase   ,       1							)
         UpperCAmelCase_									=	text_embeddings.view(bs_embed * num_images_per_prompt   ,       _lowerCAmelCase   ,       -1							)

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCAmelCase_									=	guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
             UpperCAmelCase_									=	42
             if negative_prompt is None:
                 UpperCAmelCase_									=	[''''''] * batch_size
             elif type(_lowerCAmelCase							) is not type(_lowerCAmelCase							):
                 raise TypeError(
                     F'`negative_prompt` should be the same type to `prompt`, but got {type(_lowerCAmelCase							)} !='
                     F' {type(_lowerCAmelCase							)}.'							)
             elif isinstance(_lowerCAmelCase   ,       _lowerCAmelCase							):
                 UpperCAmelCase_									=	[negative_prompt]
             elif batch_size != len(_lowerCAmelCase							):
                 raise ValueError(
                     F'`negative_prompt`: {negative_prompt} has batch size {len(_lowerCAmelCase							)}, but `prompt`:'
                     F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                     ''' the batch size of `prompt`.'''							)
             else:
                 UpperCAmelCase_									=	negative_prompt

             UpperCAmelCase_									=	text_input_ids.shape[-1]
             UpperCAmelCase_									=	self.tokenizer(
                 _lowerCAmelCase   ,       padding='''max_length'''   ,       max_length=_lowerCAmelCase   ,       truncation=_lowerCAmelCase   ,       return_tensors='''pt'''   ,       )
             UpperCAmelCase_									=	self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             UpperCAmelCase_									=	uncond_embeddings.shape[1]
             UpperCAmelCase_									=	uncond_embeddings.repeat(1   ,       _lowerCAmelCase   ,       1							)
             UpperCAmelCase_									=	uncond_embeddings.view(batch_size * num_images_per_prompt   ,       _lowerCAmelCase   ,       -1							)

             # For classifier free guidance, we need to do two forward passes.
             # Here we concatenate the unconditional and text embeddings into a single batch
             # to avoid doing two forward passes
             UpperCAmelCase_									=	torch.cat([uncond_embeddings, text_embeddings]							)

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         UpperCAmelCase_									=	(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         UpperCAmelCase_									=	text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":
                 # randn does not exist on mps
                 UpperCAmelCase_									=	torch.randn(_lowerCAmelCase   ,       generator=_lowerCAmelCase   ,       device='''cpu'''   ,       dtype=_lowerCAmelCase							).to(
                     self.device							)
             else:
                 UpperCAmelCase_									=	torch.randn(_lowerCAmelCase   ,       generator=_lowerCAmelCase   ,       device=self.device   ,       dtype=_lowerCAmelCase							)
         else:
             if latents.shape != latents_shape:
                 raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'							)
             UpperCAmelCase_									=	latents.to(self.device							)

         # set timesteps
         self.scheduler.set_timesteps(_lowerCAmelCase							)

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         UpperCAmelCase_									=	self.scheduler.timesteps.to(self.device							)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_									=	latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         UpperCAmelCase_									=	'''eta''' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
         UpperCAmelCase_									=	{}
         if accepts_eta:
             UpperCAmelCase_									=	eta

         for i, t in enumerate(self.progress_bar(_lowerCAmelCase							)							):
             # expand the latents if we are doing classifier free guidance
             UpperCAmelCase_									=	torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
             UpperCAmelCase_									=	self.scheduler.scale_model_input(_lowerCAmelCase   ,       _lowerCAmelCase							)

             # predict the noise residual
             UpperCAmelCase_									=	self.unet(_lowerCAmelCase   ,       _lowerCAmelCase   ,       encoder_hidden_states=_lowerCAmelCase							).sample

             # perform guidance
             if do_classifier_free_guidance:
                 UpperCAmelCase_					,				UpperCAmelCase_									=	noise_pred.chunk(2							)
                 UpperCAmelCase_									=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # compute the previous noisy sample x_t -> x_t-1
             UpperCAmelCase_									=	self.scheduler.step(_lowerCAmelCase   ,       _lowerCAmelCase   ,       _lowerCAmelCase   ,       **_lowerCAmelCase							).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                 callback(_lowerCAmelCase   ,       _lowerCAmelCase   ,       _lowerCAmelCase							)

         UpperCAmelCase_									=	1 / 0.18_215 * latents
         UpperCAmelCase_									=	self.vae.decode(_lowerCAmelCase							).sample

         UpperCAmelCase_									=	(image / 2 + 0.5).clamp(0   ,       1							)

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         UpperCAmelCase_									=	image.cpu().permute(0   ,       2   ,       3   ,       1							).float().numpy()

         if output_type == "pil":
             UpperCAmelCase_									=	self.numpy_to_pil(_lowerCAmelCase							)

         if not return_dict:
             return image

         return StableDiffusionPipelineOutput(images=_lowerCAmelCase   ,       nsfw_content_detected=_lowerCAmelCase							)









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






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							float     ,					_lowerCAmelCase							:							Callable     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							float = 1.0     ,					_lowerCAmelCase							:							str = None     ,					) ->			str:




     """simple docstring"""
     super().__init__()
     snake_case_            =       initial_learning_rate
     snake_case_            =       warmup_steps
     snake_case_            =       power
     snake_case_            =       decay_schedule_fn
     snake_case_            =       name



  def __call__(  self							:							Any     ,					_lowerCAmelCase							:							int	) ->			Tuple:




     """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`.
        snake_case_            =       tf.cast(_lowerCAmelCase     ,					tf.floataa	)
        snake_case_            =       tf.cast(self.warmup_steps     ,					tf.floataa	)
        snake_case_            =       global_step_float / warmup_steps_float
        snake_case_            =       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							:							Optional[Any]	) ->			Dict:




     """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    (				lowerCAmelCase_				:float		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:float = 0.0		,    lowerCAmelCase_				:float = 0.9		,    lowerCAmelCase_				:float = 0.9_9_9		,    lowerCAmelCase_				:float = 1e-8		,    lowerCAmelCase_				:Optional[float] = None		,    lowerCAmelCase_				:Optional[float] = None		,    lowerCAmelCase_				:float = 0.0		,    lowerCAmelCase_				:float = 1.0		,    lowerCAmelCase_				:Optional[List[str]] = None		,    )->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       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:
      snake_case_            =       WarmUp(
          initial_learning_rate=lowerCAmelCase_		,    decay_schedule_fn=lowerCAmelCase_		,    warmup_steps=lowerCAmelCase_		,    )
   if weight_decay_rate > 0.0:
      snake_case_            =       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:
      snake_case_            =       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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





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




     """simple docstring"""
     super().__init__(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase     ,					**_lowerCAmelCase	)
     snake_case_            =       weight_decay_rate
     snake_case_            =       include_in_weight_decay
     snake_case_            =       exclude_from_weight_decay



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Dict     ,					_lowerCAmelCase							:							Optional[Any]	) ->			Tuple:




     """simple docstring"""
     snake_case_            =       {"WarmUp": WarmUp}
     return super(_lowerCAmelCase     ,					cls	).from_config(_lowerCAmelCase     ,					custom_objects=_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							Optional[int]	) ->			List[str]:




     """simple docstring"""
     super(_lowerCAmelCase     ,					self	)._prepare_local(_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase	)
     snake_case_            =       tf.constant(
         self.weight_decay_rate     ,					name="adam_weight_decay_rate"	)



  def        lowerCAmelCase__	(  self							:							Optional[Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       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							:							List[str]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Optional[int]=None     ,					**_lowerCAmelCase							:							Dict	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       list(zip(*_lowerCAmelCase	)	)
     return super(_lowerCAmelCase     ,					self	).apply_gradients(zip(_lowerCAmelCase     ,					_lowerCAmelCase	)     ,					name=_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Union[str, Any]     ,					_lowerCAmelCase							:							Optional[Any]     ,					_lowerCAmelCase							:							Optional[int]	) ->			int:




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

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

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



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							Tuple=None	) ->			List[str]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       self._get_lr(var.device     ,					var.dtype.base_dtype     ,					_lowerCAmelCase	)
     snake_case_            =       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							:							List[Any]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Tuple=None	) ->			List[str]:




     """simple docstring"""
     snake_case_     ,     snake_case_            =       self._get_lr(var.device     ,					var.dtype.base_dtype     ,					_lowerCAmelCase	)
     snake_case_            =       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							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       super().get_config()
     config.update({"weight_decay_rate": self.weight_decay_rate}	)
     return config



  def        lowerCAmelCase__	(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[str]	) ->			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      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							str	) ->			Dict:




     """simple docstring"""
     snake_case_            =       []
     snake_case_            =       None



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			List[Any]:




     """simple docstring"""
     if self._accum_steps is None:
        snake_case_            =       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							:							Tuple	) ->			List[str]:




     """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							:							List[Any]     ,					_lowerCAmelCase							:							Union[str, Any]	) ->			Optional[Any]:




     """simple docstring"""
     if not self._gradients:
        snake_case_            =       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							:							Union[str, Any]	) ->			Optional[Any]:




     """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	)	)




