



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case     :List[str]			       =logging.get_logger(__name__)

__snake_case     :Any			       ={
    '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',
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__snake_case     :Any			       =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Optional[int]				)       ->							Optional[Any]:

							'''simple docstring'''






							for attribute in key.split('.'				):
														if is_finetuned:
																					if attribute in ["quantizer", "project_q", "project_hid"]:
																												# those layers are only relevant for pretraining and should be dropped
																												return

																					if attribute == "ctc_proj":
																												# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																												A      						=    'lm_head'

														A      						=    getattr(lowerCAmelCase__			,	lowerCAmelCase__				)

							if weight_type is not None:
														A      						=    getattr(lowerCAmelCase__			,	lowerCAmelCase__				).shape
							else:
														A      						=    hf_pointer.shape

							assert hf_shape == value.shape, (
							    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
							else:
														A      						=    value

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Any				)       ->							str:

							'''simple docstring'''






							A      						=    []
							A      						=    fairseq_model.state_dict()

							A      						=    hf_model.unispeech.feature_extractor

							for name, value in fairseq_dict.items():
														A      						=    False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	hf_model.config.feat_extract_norm == 'group'			,	)
																					A      						=    True
														else:
																					for key, mapped_key in MAPPING.items():
																												A      						=    'unispeech.' + 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(lowerCAmelCase__				)[0].split('.'				)[-2]
																																										A      						=    mapped_key.replace('*'			,	lowerCAmelCase__				)
																																			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
																																			set_recursively(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)
																												continue
														if not is_used:
																					unused_weights.append(lowerCAmelCase__				)

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	int				)       ->							Optional[int]:

							'''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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F'''{full_name} has size {value.shape}, but {feature_extractor[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:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F'''{full_name} has size {value.shape}, but'''
																					    F''' {feature_extractor[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(lowerCAmelCase__				)






@torch.no_grad()
def  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	int=None			,	lowerCAmelCase__	:	Union[str, Any]=None			,	lowerCAmelCase__	:	str=True				)       ->							str:

							'''simple docstring'''






							if config_path is not None:
														A      						=    UniSpeechConfig.from_pretrained(lowerCAmelCase__				)
							else:
														A      						=    UniSpeechConfig()

							if is_finetuned:
														if dict_path:
																					A      						=    Dictionary.load_from_json(lowerCAmelCase__				)

																					# 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(lowerCAmelCase__			,	'vocab.json'				)
																					if not os.path.isdir(lowerCAmelCase__				):
																												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase__				)				)
																												return
																					os.makedirs(lowerCAmelCase__			,	exist_ok=lowerCAmelCase__				)
																					A      						=    target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					A      						=    42
																					A      						=    43
																					with open(lowerCAmelCase__			,	'w'			,	encoding='utf-8'				) as vocab_handle:
																												json.dump(lowerCAmelCase__			,	lowerCAmelCase__				)
																					A      						=    WavaVecaPhonemeCTCTokenizer(
																					    lowerCAmelCase__			,	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=lowerCAmelCase__			,	)
																					A      						=    True if config.feat_extract_norm == 'layer' else False
																					A      						=    WavaVecaFeatureExtractor(
																					    feature_size=1			,	sampling_rate=16000			,	padding_value=0			,	do_normalize=lowerCAmelCase__			,	return_attention_mask=lowerCAmelCase__			,	)
																					A      						=    WavaVecaProcessor(feature_extractor=lowerCAmelCase__			,	tokenizer=lowerCAmelCase__				)
																					processor.save_pretrained(lowerCAmelCase__				)

														A      						=    UniSpeechForCTC(lowerCAmelCase__				)
							else:
														A      						=    UniSpeechForPreTraining(lowerCAmelCase__				)

							if is_finetuned:
														A						,   A						,   A      						=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]			,	arg_overrides={'data': '/'.join(dict_path.split('/'				)[:-1]				), 'w2v_path': checkpoint_path}				)
							else:
														A						,   A						,   A      						=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

							A      						=    model[0].eval()

							recursively_load_weights(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							hf_unispeech.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :Any			       =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'
				)
				__snake_case     :Optional[Any]			       =parser.parse_args()
				convert_unispeech_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
				)



'''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_      )      )






