



"""simple docstring"""







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCamelCase     =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]

def       _lowerCamelCase				(							UpperCAmelCase_ :       List[Any]     )	-> Tuple:






	"""simple docstring"""





	A__											=					{}
	with open(UpperCAmelCase_,	"r"     ) as file:
		for line_number, line in enumerate(UpperCAmelCase_     ):
			A__											=					line.strip()
			if line:
				A__											=					line.split()
				A__											=					line_number
				A__											=					words[0]
				A__											=					value
	return result

def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       Any     )	-> Dict:






	"""simple docstring"""





	for attribute in key.split("."     ):
		A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     )

	A__											=					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(UpperCAmelCase_     ):
			A__											=					PARAM_MAPPING[full_name.split("."     )[-1]]
			A__											=					'param'

	if weight_type is not None and weight_type != "param":
		A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     ).shape
	elif weight_type is not None and weight_type == "param":
		A__											=					hf_pointer
		for attribute in hf_param_name.split("."     ):
			A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     )
		A__											=					shape_pointer.shape

		# let's reduce dimension
		A__											=					value[0]
	else:
		A__											=					hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
		    F""" {value.shape} for {full_name}"""     )

	if weight_type == "weight":
		A__											=					value
	elif weight_type == "weight_g":
		A__											=					value
	elif weight_type == "weight_v":
		A__											=					value
	elif weight_type == "bias":
		A__											=					value
	elif weight_type == "param":
		for attribute in hf_param_name.split("."     ):
			A__											=					getattr(UpperCAmelCase_,	UpperCAmelCase_     )
		A__											=					value
	else:
		A__											=					value

	logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       Optional[Any],	UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       int     )	-> Dict:






	"""simple docstring"""





	A__											=					None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(UpperCAmelCase_     ):
			A__											=					PARAM_MAPPING[full_name.split("."     )[-1]]
			A__											=					'param'

	if weight_type is not None and weight_type != "param":
		A__											=					'.'.join([key, weight_type]     )
	elif weight_type is not None and weight_type == "param":
		A__											=					'.'.join([key, hf_param_name]     )
	else:
		A__											=					key

	A__											=					value if 'lm_head' in full_key else value[0]


UpperCamelCase     =							{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}

def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Optional[Any]=None,	UpperCAmelCase_ :       Tuple=None     )	-> List[Any]:






	"""simple docstring"""





	A__											=					False
	for key, mapped_key in MAPPING.items():
		A__											=					'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
		if key in name or key.split("w2v_model."     )[-1] == name.split("."     )[0]:
			A__											=					True
			if "*" in mapped_key:
				A__											=					name.split(UpperCAmelCase_     )[0].split("."     )[-2]
				A__											=					mapped_key.replace("*",	UpperCAmelCase_     )
			if "weight_g" in name:
				A__											=					'weight_g'
			elif "weight_v" in name:
				A__											=					'weight_v'
			elif "bias" in name:
				A__											=					'bias'
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				A__											=					'weight'
			else:
				A__											=					None
			if hf_dict is not None:
				rename_dict(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
			else:
				set_recursively(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
			return is_used
	return is_used

def       _lowerCamelCase				(							UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       str     )	-> Any:






	"""simple docstring"""





	A__											=					[]
	A__											=					fairseq_model.state_dict()

	A__											=					hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		A__											=					False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_,	hf_model.config.feat_extract_norm == "group",	)
			A__											=					True
		else:
			A__											=					load_wavaveca_layer(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )
		if not is_used:
			unused_weights.append(UpperCAmelCase_     )

	logger.warning(F"""Unused weights: {unused_weights}"""     )

def       _lowerCamelCase				(							UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Tuple,	UpperCAmelCase_ :       List[str],	UpperCAmelCase_ :       Tuple     )	-> Union[str, Any]:






	"""simple docstring"""





	A__											=					full_name.split("conv_layers."     )[-1]
	A__											=					name.split("."     )
	A__											=					int(items[0]     )
	A__											=					int(items[1]     )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F"""{full_name} has size {value.shape}, but"""
				    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""     )
			A__											=					value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F"""{full_name} has size {value.shape}, but"""
				    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""     )
			A__											=					value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""     )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F"""{full_name} has size {value.shape}, but"""
				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
			A__											=					value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F"""{full_name} has size {value.shape}, but"""
				    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
			A__											=					value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
	else:
		unused_weights.append(UpperCAmelCase_     )

@torch.no_grad()
def       _lowerCamelCase				(							UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       Union[str, Any]=None,	UpperCAmelCase_ :       List[str]=None,	UpperCAmelCase_ :       Union[str, Any]=True,	UpperCAmelCase_ :       List[str]=False     )	-> Optional[Any]:






	"""simple docstring"""





	if config_path is not None:
		A__											=					WavaVecaConfig.from_pretrained(UpperCAmelCase_     )
	else:
		A__											=					WavaVecaConfig()

	if is_seq_class:
		A__											=					read_txt_into_dict(UpperCAmelCase_     )
		A__											=					idalabel
		A__											=					WavaVecaForSequenceClassification(UpperCAmelCase_     )
		A__											=					WavaVecaFeatureExtractor(
		    feature_size=1,	sampling_rate=16000,	padding_value=0,	do_normalize=UpperCAmelCase_,	return_attention_mask=UpperCAmelCase_,	)
		feature_extractor.save_pretrained(UpperCAmelCase_     )

	elif is_finetuned:
		if dict_path:
			A__											=					Dictionary.load(UpperCAmelCase_     )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			A__											=					target_dict.pad_index
			A__											=					target_dict.bos_index
			A__											=					target_dict.eos_index
			A__											=					len(target_dict.symbols     )
			A__											=					os.path.join(UpperCAmelCase_,	"vocab.json"     )
			if not os.path.isdir(UpperCAmelCase_     ):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(UpperCAmelCase_     )     )
				return
			os.makedirs(UpperCAmelCase_,	exist_ok=UpperCAmelCase_     )
			A__											=					target_dict.indices

			# fairseq has the <pad> and <s> switched
			A__											=					0
			A__											=					1
			with open(UpperCAmelCase_,	"w",	encoding="utf-8"     ) as vocab_handle:
				json.dump(UpperCAmelCase_,	UpperCAmelCase_     )
			A__											=					WavaVecaCTCTokenizer(
			    UpperCAmelCase_,	unk_token=target_dict.unk_word,	pad_token=target_dict.pad_word,	bos_token=target_dict.bos_word,	eos_token=target_dict.eos_word,	word_delimiter_token="|",	do_lower_case=UpperCAmelCase_,	)
			A__											=					True if config.feat_extract_norm == 'layer' else False
			A__											=					WavaVecaFeatureExtractor(
			    feature_size=1,	sampling_rate=16000,	padding_value=0,	do_normalize=UpperCAmelCase_,	return_attention_mask=UpperCAmelCase_,	)
			A__											=					WavaVecaProcessor(feature_extractor=UpperCAmelCase_,	tokenizer=UpperCAmelCase_     )
			processor.save_pretrained(UpperCAmelCase_     )

		A__											=					WavaVecaForCTC(UpperCAmelCase_     )
	else:
		A__											=					WavaVecaForPreTraining(UpperCAmelCase_     )

	if is_finetuned or is_seq_class:
		A__											=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path],	arg_overrides={"data": "/".join(dict_path.split("/"     )[:-1]     )}     )
	else:
		A__											=					argparse.Namespace(task="audio_pretraining"     )
		A__											=					fairseq.tasks.setup_task(UpperCAmelCase_     )

		A__											=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],	task=UpperCAmelCase_     )

	A__											=					model[0].eval()

	recursively_load_weights(UpperCAmelCase_,	UpperCAmelCase_,	not is_finetuned     )

	hf_wavavec.save_pretrained(UpperCAmelCase_     )


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
			parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
			parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
			parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
			parser.add_argument(
			    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
			)
			parser.add_argument(
			    """--is_seq_class""",
			    action="""store_true""",
			    help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
			)
			UpperCamelCase     =							parser.parse_args()

			UpperCamelCase     =							not args.not_finetuned and not args.is_seq_class
			convert_wavaveca_checkpoint(
			    args.checkpoint_path,
			    args.pytorch_dump_folder_path,
			    args.config_path,
			    args.dict_path,
			    is_finetuned,
			    args.is_seq_class,
			)








'''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   UpperCAmelCase_    (tf.keras.optimizers.schedules.LearningRateSchedule    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	Any:
         super().__init__()
         __lowerCamelCase  :      Optional[Any]														=						initial_learning_rate
         __lowerCamelCase  :      Optional[Any]														=						warmup_steps
         __lowerCamelCase  :      Union[str, Any]														=						power
         __lowerCamelCase  :      Optional[int]														=						decay_schedule_fn
         __lowerCamelCase  :      Any														=						name







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         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`.
              __lowerCamelCase  :      str														=						tf.cast(SCREAMING_SNAKE_CASE_     ,	tf.floataa      )
              __lowerCamelCase  :      Optional[int]														=						tf.cast(self.warmup_steps     ,	tf.floataa      )
              __lowerCamelCase  :      List[Any]														=						global_step_float / warmup_steps_float
              __lowerCamelCase  :      Optional[Any]														=						self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE_     ,	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=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	Optional[Any]:
         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__   (      UpperCAmelCase_       :							float ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							float = 0.9 ,       UpperCAmelCase_       :							float = 0.999 ,       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 ,       )	->						int:
     __lowerCamelCase  :      int														=						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:
          __lowerCamelCase  :      str														=						WarmUp(
              initial_learning_rate=UpperCAmelCase_ ,       decay_schedule_fn=UpperCAmelCase_ ,       warmup_steps=UpperCAmelCase_ ,       )
     if weight_decay_rate > 0.0:
          __lowerCamelCase  :      List[Any]														=						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:
          __lowerCamelCase  :      Tuple														=						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   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_ = 0.0_0_1     ,	SCREAMING_SNAKE_CASE_ = 0.9     ,	SCREAMING_SNAKE_CASE_ = 0.9_9_9     ,	SCREAMING_SNAKE_CASE_ = 1E-7     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "AdamWeightDecay"     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	int:
         super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						weight_decay_rate
         __lowerCamelCase  :      str														=						include_in_weight_decay
         __lowerCamelCase  :      List[Any]														=						exclude_from_weight_decay







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase  :      Any														=						{'WarmUp': WarmUp}
         return super(SCREAMING_SNAKE_CASE_     ,	cls      ).from_config(SCREAMING_SNAKE_CASE_     ,	custom_objects=SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         super(SCREAMING_SNAKE_CASE_     ,	self      )._prepare_local(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tf.constant(
             self.weight_decay_rate     ,	name='adam_weight_decay_rate'      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      Tuple														=						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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						list(zip(*SCREAMING_SNAKE_CASE_      )      )
         return super(SCREAMING_SNAKE_CASE_     ,	self      ).apply_gradients(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )     ,	name=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if apply_state is None:
              return self._decayed_lr_t[var_dtype], {}

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

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







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	str:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_dense(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	Union[str, Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_sparse(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						super().get_config()
         config.update({'weight_decay_rate': self.weight_decay_rate}      )
         return config







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) is not None:
                        return True

         if self._exclude_from_weight_decay:
              for r in self._exclude_from_weight_decay:
                   if re.search(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) is not None:
                        return False
         return True







class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self      )							->	Tuple:
         __lowerCamelCase  :      Tuple														=						[]
         __lowerCamelCase  :      Optional[Any]														=						None







    @property
    def  lowercase_							(				self      )							->	List[str]:
         if self._accum_steps is None:
              __lowerCamelCase  :      Tuple														=						tf.Variable(
                  tf.constant(0     ,	dtype=tf.intaa      )     ,	trainable=SCREAMING_SNAKE_CASE_     ,	synchronization=tf.VariableSynchronization.ON_READ     ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,	)

         return self._accum_steps.value()







    @property
    def  lowercase_							(				self      )							->	List[str]:
         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     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if not self._gradients:
              __lowerCamelCase  :      List[str]														=						self.step  # Create the step variable.
              self._gradients.extend(
                  [
                      tf.Variable(
                          tf.zeros_like(SCREAMING_SNAKE_CASE_      )     ,	trainable=SCREAMING_SNAKE_CASE_     ,	synchronization=tf.VariableSynchronization.ON_READ     ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,	)
                      if gradient is not None
                      else gradient
                      for gradient in gradients
                  ]      )
         if len(SCREAMING_SNAKE_CASE_      ) != len(self._gradients      ):
              raise ValueError(f'Expected {len(self._gradients      )} gradients, but got {len(SCREAMING_SNAKE_CASE_      )}'      )

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

         self._accum_steps.assign_add(1      )







    def  lowercase_							(				self      )							->	int:
         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(SCREAMING_SNAKE_CASE_      )      )






