
'''simple docstring'''


import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       int=1):
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split('''.''')[n_shave_prefix_segments:])
   else:
      return ".".join(path.split('''.''')[:n_shave_prefix_segments])



def  __snake_case     (    _UpperCAmelCase					:       Optional[int],      _UpperCAmelCase					:       Optional[int]=0):
   UpperCamelCase       =  []
   for old_item in old_list:
      UpperCamelCase       =  old_item.replace('''in_layers.0''',      '''norm1''')
      UpperCamelCase       =  new_item.replace('''in_layers.2''',      '''conv1''')

      UpperCamelCase       =  new_item.replace('''out_layers.0''',      '''norm2''')
      UpperCamelCase       =  new_item.replace('''out_layers.3''',      '''conv2''')

      UpperCamelCase       =  new_item.replace('''emb_layers.1''',      '''time_emb_proj''')
      UpperCamelCase       =  new_item.replace('''skip_connection''',      '''conv_shortcut''')

      UpperCamelCase       =  shave_segments(_UpperCAmelCase,      n_shave_prefix_segments=_UpperCAmelCase)

      mapping.append({'''old''': old_item, '''new''': new_item})

   return mapping



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Optional[int]=0):
   UpperCamelCase       =  []
   for old_item in old_list:
      UpperCamelCase       =  old_item

      UpperCamelCase       =  new_item.replace('''norm.weight''',      '''group_norm.weight''')
      UpperCamelCase       =  new_item.replace('''norm.bias''',      '''group_norm.bias''')

      UpperCamelCase       =  new_item.replace('''proj_out.weight''',      '''proj_attn.weight''')
      UpperCamelCase       =  new_item.replace('''proj_out.bias''',      '''proj_attn.bias''')

      UpperCamelCase       =  shave_segments(_UpperCAmelCase,      n_shave_prefix_segments=_UpperCAmelCase)

      mapping.append({'''old''': old_item, '''new''': new_item})

   return mapping



def  __snake_case     (    _UpperCAmelCase					:       List[Any],      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       Optional[int]=None,      _UpperCAmelCase					:       List[str]=None,      _UpperCAmelCase					:       Dict=None):
   assert isinstance(_UpperCAmelCase,      _UpperCAmelCase), "Paths should be a list of dicts containing 'old' and 'new' keys."

   # Splits the attention layers into three variables.
   if attention_paths_to_split is not None:
      for path, path_map in attention_paths_to_split.items():
         UpperCamelCase       =  old_checkpoint[path]
         UpperCamelCase       =  old_tensor.shape[0] // 3

         UpperCamelCase       =  (-1, channels) if len(old_tensor.shape) == 3 else (-1)

         UpperCamelCase       =  old_tensor.shape[0] // config['''num_head_channels'''] // 3

         UpperCamelCase       =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
         UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  old_tensor.split(channels // num_heads,      dim=1)

         UpperCamelCase       =  query.reshape(_UpperCAmelCase)
         UpperCamelCase       =  key.reshape(_UpperCAmelCase)
         UpperCamelCase       =  value.reshape(_UpperCAmelCase)

   for path in paths:
      UpperCamelCase       =  path['''new''']

      # These have already been assigned
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      # Global renaming happens here
      UpperCamelCase       =  new_path.replace('''middle_block.0''',      '''mid_block.resnets.0''')
      UpperCamelCase       =  new_path.replace('''middle_block.1''',      '''mid_block.attentions.0''')
      UpperCamelCase       =  new_path.replace('''middle_block.2''',      '''mid_block.resnets.1''')

      if additional_replacements is not None:
         for replacement in additional_replacements:
            UpperCamelCase       =  new_path.replace(replacement['''old'''],      replacement['''new'''])

        # proj_attn.weight has to be converted from conv 1D to linear
      if "proj_attn.weight" in new_path:
         UpperCamelCase       =  old_checkpoint[path['''old''']][:, :, 0]
      else:
         UpperCamelCase       =  old_checkpoint[path['''old''']]



def  __snake_case     (    _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       Tuple):
   UpperCamelCase       =  {}

   UpperCamelCase       =  checkpoint['''time_embed.0.weight''']
   UpperCamelCase       =  checkpoint['''time_embed.0.bias''']
   UpperCamelCase       =  checkpoint['''time_embed.2.weight''']
   UpperCamelCase       =  checkpoint['''time_embed.2.bias''']

   UpperCamelCase       =  checkpoint['''input_blocks.0.0.weight''']
   UpperCamelCase       =  checkpoint['''input_blocks.0.0.bias''']

   UpperCamelCase       =  checkpoint['''out.0.weight''']
   UpperCamelCase       =  checkpoint['''out.0.bias''']
   UpperCamelCase       =  checkpoint['''out.2.weight''']
   UpperCamelCase       =  checkpoint['''out.2.bias''']

   # Retrieves the keys for the input blocks only
   UpperCamelCase       =  len({'''.'''.join(layer.split('''.''')[:2]) for layer in checkpoint if '''input_blocks''' in layer})
   UpperCamelCase       =  {
       layer_id: [key for key in checkpoint if f'input_blocks.{layer_id}' in key]
       for layer_id in range(_UpperCAmelCase)
   }

   # Retrieves the keys for the middle blocks only
   UpperCamelCase       =  len({'''.'''.join(layer.split('''.''')[:2]) for layer in checkpoint if '''middle_block''' in layer})
   UpperCamelCase       =  {
       layer_id: [key for key in checkpoint if f'middle_block.{layer_id}' in key]
       for layer_id in range(_UpperCAmelCase)
   }

   # Retrieves the keys for the output blocks only
   UpperCamelCase       =  len({'''.'''.join(layer.split('''.''')[:2]) for layer in checkpoint if '''output_blocks''' in layer})
   UpperCamelCase       =  {
       layer_id: [key for key in checkpoint if f'output_blocks.{layer_id}' in key]
       for layer_id in range(_UpperCAmelCase)
   }

   for i in range(1,      _UpperCAmelCase):
      UpperCamelCase       =  (i - 1) // (config['''num_res_blocks'''] + 1)
      UpperCamelCase       =  (i - 1) % (config['''num_res_blocks'''] + 1)

      UpperCamelCase       =  [key for key in input_blocks[i] if f'input_blocks.{i}.0' in key]
      UpperCamelCase       =  [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]

      if f'input_blocks.{i}.0.op.weight' in checkpoint:
         UpperCamelCase       =  checkpoint[
             f'input_blocks.{i}.0.op.weight'
         ]
         UpperCamelCase       =  checkpoint[
             f'input_blocks.{i}.0.op.bias'
         ]
         continue

      UpperCamelCase       =  renew_resnet_paths(_UpperCAmelCase)
      UpperCamelCase       =  {'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
      UpperCamelCase       =  {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
      assign_to_checkpoint(
          _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      additional_replacements=[meta_path, resnet_op],      config=_UpperCAmelCase)

      if len(_UpperCAmelCase):
         UpperCamelCase       =  renew_attention_paths(_UpperCAmelCase)
         UpperCamelCase       =  {
             '''old''': f'input_blocks.{i}.1',
             '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}',
         }
         UpperCamelCase       =  {
             f'input_blocks.{i}.1.qkv.bias': {
                 '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                 '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                 '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
             },
             f'input_blocks.{i}.1.qkv.weight': {
                 '''key''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                 '''query''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                 '''value''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
             },
         }
         assign_to_checkpoint(
             _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      additional_replacements=[meta_path],      attention_paths_to_split=_UpperCAmelCase,      config=_UpperCAmelCase,      )

   UpperCamelCase       =  middle_blocks[0]
   UpperCamelCase       =  middle_blocks[1]
   UpperCamelCase       =  middle_blocks[2]

   UpperCamelCase       =  renew_resnet_paths(_UpperCAmelCase)
   assign_to_checkpoint(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      config=_UpperCAmelCase)

   UpperCamelCase       =  renew_resnet_paths(_UpperCAmelCase)
   assign_to_checkpoint(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      config=_UpperCAmelCase)

   UpperCamelCase       =  renew_attention_paths(_UpperCAmelCase)
   UpperCamelCase       =  {
       '''middle_block.1.qkv.bias''': {
           '''key''': '''mid_block.attentions.0.key.bias''',
           '''query''': '''mid_block.attentions.0.query.bias''',
           '''value''': '''mid_block.attentions.0.value.bias''',
       },
       '''middle_block.1.qkv.weight''': {
           '''key''': '''mid_block.attentions.0.key.weight''',
           '''query''': '''mid_block.attentions.0.query.weight''',
           '''value''': '''mid_block.attentions.0.value.weight''',
       },
   }
   assign_to_checkpoint(
       _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      attention_paths_to_split=_UpperCAmelCase,      config=_UpperCAmelCase)

   for i in range(_UpperCAmelCase):
      UpperCamelCase       =  i // (config['''num_res_blocks'''] + 1)
      UpperCamelCase       =  i % (config['''num_res_blocks'''] + 1)
      UpperCamelCase       =  [shave_segments(_UpperCAmelCase,      2) for name in output_blocks[i]]
      UpperCamelCase       =  {}

      for layer in output_block_layers:
         UpperCamelCase     , UpperCamelCase       =  layer.split('''.''')[0], shave_segments(_UpperCAmelCase,      1)
         if layer_id in output_block_list:
            output_block_list[layer_id].append(_UpperCAmelCase)
         else:
            UpperCamelCase       =  [layer_name]

      if len(_UpperCAmelCase) > 1:
         UpperCamelCase       =  [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
         UpperCamelCase       =  [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]

         UpperCamelCase       =  renew_resnet_paths(_UpperCAmelCase)
         UpperCamelCase       =  renew_resnet_paths(_UpperCAmelCase)

         UpperCamelCase       =  {'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
         assign_to_checkpoint(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      additional_replacements=[meta_path],      config=_UpperCAmelCase)

         if ["conv.weight", "conv.bias"] in output_block_list.values():
            UpperCamelCase       =  list(output_block_list.values()).index(['''conv.weight''', '''conv.bias'''])
            UpperCamelCase       =  checkpoint[
                f'output_blocks.{i}.{index}.conv.weight'
            ]
            UpperCamelCase       =  checkpoint[
                f'output_blocks.{i}.{index}.conv.bias'
            ]

            # Clear attentions as they have been attributed above.
            if len(_UpperCAmelCase) == 2:
               UpperCamelCase       =  []

         if len(_UpperCAmelCase):
            UpperCamelCase       =  renew_attention_paths(_UpperCAmelCase)
            UpperCamelCase       =  {
                '''old''': f'output_blocks.{i}.1',
                '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
            }
            UpperCamelCase       =  {
                f'output_blocks.{i}.1.qkv.bias': {
                    '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                    '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                    '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                },
                f'output_blocks.{i}.1.qkv.weight': {
                    '''key''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                    '''query''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                    '''value''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                },
            }
            assign_to_checkpoint(
                _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      additional_replacements=[meta_path],      attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions) else None,      config=_UpperCAmelCase,      )
      else:
         UpperCamelCase       =  renew_resnet_paths(_UpperCAmelCase,      n_shave_prefix_segments=1)
         for path in resnet_0_paths:
            UpperCamelCase       =  '''.'''.join(['''output_blocks''', str(_UpperCAmelCase), path['''old''']])
            UpperCamelCase       =  '''.'''.join(['''up_blocks''', str(_UpperCAmelCase), '''resnets''', str(_UpperCAmelCase), path['''new''']])

            UpperCamelCase       =  checkpoint[old_path]

   return new_checkpoint


if __name__ == "__main__":
   snake_case_ :  Any     =					argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
   )

   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help='The config json file corresponding to the architecture.',
   )

   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

   snake_case_ :  List[str]     =					parser.parse_args()

   snake_case_ :  Optional[Any]     =					torch.load(args.checkpoint_path)

   with open(args.config_file) as f:
      snake_case_ :  List[str]     =					json.loads(f.read())

   snake_case_ :  List[str]     =					convert_ldm_checkpoint(checkpoint, config)

   if "ldm" in config:
      del config["ldm"]

   snake_case_ :  List[str]     =					UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      snake_case_ :  Dict     =					DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
      snake_case_ :  Union[str, Any]     =					VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

      snake_case_ :  Any     =					LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)







'''simple docstring'''


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

import tensorflow as tf


try:
   from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
   from tensorflow.keras.optimizers import Adam


class 						lowercase__	(  tf.keras.optimizers.schedules.LearningRateSchedule   ):






   '''simple docstring'''







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

      '''simple docstring'''







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







   def __call__(			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      with tf.name_scope(self.name or '''WarmUp'''   ) as name:
         # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
         # learning rate will be `global_step/num_warmup_steps * init_lr`.
         UpperCamelCase       =  tf.cast(lowerCamelCase__ ,   tf.floataa   )
         UpperCamelCase       =  tf.cast(self.warmup_steps ,   tf.floataa   )
         UpperCamelCase       =  global_step_float / warmup_steps_float
         UpperCamelCase       =  self.initial_learning_rate * tf.math.pow(lowerCamelCase__ ,   self.power   )
         return tf.cond(
             global_step_float < warmup_steps_float ,   lambda: warmup_learning_rate ,   lambda: self.decay_schedule_fn(step - self.warmup_steps   ) ,   name=lowerCamelCase__ ,   )







   def       UpperCAmelCase				(			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  __snake_case     (    _UpperCAmelCase					:       float,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       float = 0.0,      _UpperCAmelCase					:       float = 0.9,      _UpperCAmelCase					:       float = 0.9_9_9,      _UpperCAmelCase					:       float = 1E-8,      _UpperCAmelCase					:       Optional[float] = None,      _UpperCAmelCase					:       Optional[float] = None,      _UpperCAmelCase					:       float = 0.0,      _UpperCAmelCase					:       float = 1.0,      _UpperCAmelCase					:       Optional[List[str]] = None,      ):
   UpperCamelCase       =  tf.keras.optimizers.schedules.PolynomialDecay(
       initial_learning_rate=_UpperCAmelCase,      decay_steps=num_train_steps - num_warmup_steps,      end_learning_rate=init_lr * min_lr_ratio,      power=_UpperCAmelCase,      )
   if num_warmup_steps:
      UpperCamelCase       =  WarmUp(
          initial_learning_rate=_UpperCAmelCase,      decay_schedule_fn=_UpperCAmelCase,      warmup_steps=_UpperCAmelCase,      )
   if weight_decay_rate > 0.0:
      UpperCamelCase       =  AdamWeightDecay(
          learning_rate=_UpperCAmelCase,      weight_decay_rate=_UpperCAmelCase,      beta_a=_UpperCAmelCase,      beta_a=_UpperCAmelCase,      epsilon=_UpperCAmelCase,      clipnorm=_UpperCAmelCase,      global_clipnorm=_UpperCAmelCase,      exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''],      include_in_weight_decay=_UpperCAmelCase,      )
   else:
      UpperCamelCase       =  tf.keras.optimizers.Adam(
          learning_rate=_UpperCAmelCase,      beta_a=_UpperCAmelCase,      beta_a=_UpperCAmelCase,      epsilon=_UpperCAmelCase,      clipnorm=_UpperCAmelCase,      global_clipnorm=_UpperCAmelCase,      )
   # 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 						lowercase__	(  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__ ,   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   **lowerCamelCase__   )
      UpperCamelCase       =  weight_decay_rate
      UpperCamelCase       =  include_in_weight_decay
      UpperCamelCase       =  exclude_from_weight_decay







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  {'''WarmUp''': WarmUp}
      return super(lowerCamelCase__ ,   cls   ).from_config(lowerCamelCase__ ,   custom_objects=lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      super(lowerCamelCase__ ,   self   )._prepare_local(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
      UpperCamelCase       =  tf.constant(
          self.weight_decay_rate ,   name='''adam_weight_decay_rate'''   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self._do_use_weight_decay(var.name   )
      if do_decay:
         return var.assign_sub(
             learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['''weight_decay_rate'''] ,   use_locking=self._use_locking ,   )
      return tf.no_op()







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__=None ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase     , UpperCamelCase       =  list(zip(*lowerCamelCase__   )   )
      return super(lowerCamelCase__ ,   self   ).apply_gradients(zip(lowerCamelCase__ ,   lowerCamelCase__   ) ,   name=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







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

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

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__   ):

      '''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 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  []
      UpperCamelCase       =  None







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self._accum_steps is None:
         UpperCamelCase       =  tf.Variable(
             tf.constant(0 ,   dtype=tf.intaa   ) ,   trainable=lowerCamelCase__ ,   synchronization=tf.VariableSynchronization.ON_READ ,   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA ,   )

      return self._accum_steps.value()







   @property
   def       UpperCAmelCase				(			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 ,   lowerCamelCase__   ):

      '''simple docstring'''







      if not self._gradients:
         UpperCamelCase       =  self.step  # Create the step variable.
         self._gradients.extend(
             [
                 tf.Variable(
                     tf.zeros_like(lowerCamelCase__   ) ,   trainable=lowerCamelCase__ ,   synchronization=tf.VariableSynchronization.ON_READ ,   aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA ,   )
                 if gradient is not None
                 else gradient
                 for gradient in gradients
             ]   )
      if len(lowerCamelCase__   ) != len(self._gradients   ):
         raise ValueError(f'Expected {len(self._gradients   )} gradients, but got {len(lowerCamelCase__   )}'   )

      for accum_gradient, gradient in zip(self._gradients ,   lowerCamelCase__   ):
         if accum_gradient is not None and gradient is not None:
            accum_gradient.assign_add(lowerCamelCase__   )

      self._accum_steps.assign_add(1   )







   def       UpperCAmelCase				(			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(lowerCamelCase__   )   )





