




'''simple docstring'''


import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


_snake_case				:				List[Any]       							=		logging.get_logger(__name__)
def 		snake_case_   (UpperCamelCase	:  Tuple			,						UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							_a     					=    nn.ModuleList([src_layers[i] for i in layers_to_copy]				)
							assert len(a__				) == len(a__				), f'{len(a__				)} != {len(a__				)}'
							dest_layers.load_state_dict(layers_to_copy.state_dict()				)


_snake_case				:				List[str]       							=		{
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
_snake_case				:				List[str]       							=		{
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}
def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							try:
														_a     					=    LAYERS_TO_COPY[n_teacher][n_student]
														return val
							except KeyError:
														if n_student != n_teacher:
																					warnings.warn(
																					    f'no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first'
																					    f' {n_student}'				)
														return list(range(a__				)				)
def 		snake_case_   (UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  Optional[Any]				):





							'''simple docstring'''


							if n_student > n_teacher:
														raise ValueError(f'Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}'				)
							elif n_teacher == n_student:
														return list(range(a__				)				)
							elif n_student == 1:
														return [n_teacher - 1]
							else:
														return LAYERS_TO_SUPERVISE[n_teacher][n_student]
def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  List[Any] = "student"			,						UpperCamelCase	:  List[str] = None			,						UpperCamelCase	:  int = None			,						UpperCamelCase	:  Any=False			,						UpperCamelCase	:  Optional[int]=None			,						UpperCamelCase	:  Optional[Any]=None			,						**UpperCamelCase	:  Any			,						):





							'''simple docstring'''


							_a     					=    '''encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher.'''
							assert (e is not None) or (d is not None), _msg
							if isinstance(a__			,						a__				):
														AutoTokenizer.from_pretrained(a__				).save_pretrained(a__				)  # purely for convenience
														_a     					=    AutoModelForSeqaSeqLM.from_pretrained(a__				).eval()
							else:
														assert isinstance(a__			,						a__				), f'teacher must be a model or string got type {type(a__				)}'
							_a     					=    teacher.config.to_diff_dict()

							try:
														_a     					=    teacher.config.encoder_layers, teacher.config.decoder_layers
														if e is None:
																					_a     					=    teacher_e
														if d is None:
																					_a     					=    teacher_d
														init_kwargs.update({'''encoder_layers''': e, '''decoder_layers''': d}				)
							except AttributeError:  # T5
														if hasattr(teacher.config			,						'''num_encoder_layers'''				):
																					_a     					=    teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
														else:
																					_a     					=    teacher.config.num_layers, teacher.config.num_decoder_layers
														if e is None:
																					_a     					=    teacher_e
														if d is None:
																					_a     					=    teacher_d
														if hasattr(teacher.config			,						'''num_encoder_layers'''				):
																					init_kwargs.update({'''num_encoder_layers''': e, '''num_decoder_layers''': d}				)
														else:
																					init_kwargs.update({'''num_layers''': e, '''num_decoder_layers''': d}				)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
							init_kwargs.update(a__				)

							# Copy weights
							_a     					=    teacher.config_class(**a__				)
							_a     					=    AutoModelForSeqaSeqLM.from_config(a__				)
							# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
							_a     					=    student.load_state_dict(teacher.state_dict()			,						strict=a__				)
							assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

							if copy_first_teacher_layers:  # Our copying is done. We just log and save
														_a     					=    list(range(a__				)				), list(range(a__				)				)
														logger.info(
														    f'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to'
														    f' {save_path}'				)
														student.save_pretrained(a__				)
														return student, e_layers_to_copy, d_layers_to_copy

							# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
							if e_layers_to_copy is None:
														_a     					=    pick_layers_to_copy(a__			,						a__				)
							if d_layers_to_copy is None:
														_a     					=    pick_layers_to_copy(a__			,						a__				)

							try:
														if hasattr(
														    a__			,						'''prophetnet'''				):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
																					copy_layers(teacher.prophetnet.encoder.layers			,						student.prophetnet.encoder.layers			,						a__				)
																					copy_layers(teacher.prophetnet.decoder.layers			,						student.prophetnet.decoder.layers			,						a__				)
														else:
																					copy_layers(teacher.model.encoder.layers			,						student.model.encoder.layers			,						a__				)
																					copy_layers(teacher.model.decoder.layers			,						student.model.decoder.layers			,						a__				)
							except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
														copy_layers(teacher.encoder.block			,						student.encoder.block			,						a__				)
														copy_layers(teacher.decoder.block			,						student.decoder.block			,						a__				)
							logger.info(
							    f'Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}'				)
							_a     					=    {
							    '''teacher_type''': teacher.config.model_type,
							    '''copied_encoder_layers''': e_layers_to_copy,
							    '''copied_decoder_layers''': d_layers_to_copy,
							}
							student.save_pretrained(a__				)
							# Save information about copying for easier reproducibility

							return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
				fire.Fire(create_student_by_copying_alternating_layers)







'''simple docstring'''


import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule

class       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    jnp.floataa
							lowercase_          =    True







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Dict:


														"""simple docstring"""


														super().setup()
														_a     					=    nn.Dense(5							,      dtype=self.dtype )







							def __call__(  self	:  List[Any]							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Dict )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    super().__call__(*lowerCAmelCase_							,      **lowerCAmelCase_ )
														_a     					=    self.cls(outputs[2] )
														return outputs[:2] + (cls_out,)



class       A   (		_a   ):
							lowercase_          =    FlaxBigBirdForNaturalQuestionsModule



def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  str			,						UpperCamelCase	:  int			,						UpperCamelCase	:  Tuple			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							def cross_entropy(UpperCamelCase	:  int			,						UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  List[str]=None				):
														_a     					=    logits.shape[-1]
														_a     					=    (labels[..., None] == jnp.arange(UpperCamelCase				)[None]).astype('''f4'''				)
														_a     					=    jax.nn.log_softmax(UpperCamelCase			,						axis=-1				)
														_a     					=    -jnp.sum(labels * logits			,						axis=-1				)
														if reduction is not None:
																					_a     					=    reduction(UpperCamelCase				)
														return loss

							_a     					=    partial(UpperCamelCase			,						reduction=jnp.mean				)
							_a     					=    cross_entropy(UpperCamelCase			,						UpperCamelCase				)
							_a     					=    cross_entropy(UpperCamelCase			,						UpperCamelCase				)
							_a     					=    cross_entropy(UpperCamelCase			,						UpperCamelCase				)
							return (start_loss + end_loss + pooled_loss) / 3

@dataclass
class       A   :
							lowercase_          =    "google/bigbird-roberta-base"
							lowercase_          =    3000
							lowercase_          =    1_0500

							lowercase_          =    128
							lowercase_          =    3

							lowercase_          =    1
							lowercase_          =    5

							# tx_args
							lowercase_          =    3e-5
							lowercase_          =    0.0
							lowercase_          =    2_0000
							lowercase_          =    0.0095

							lowercase_          =    "bigbird-roberta-natural-questions"
							lowercase_          =    "training-expt"
							lowercase_          =    "data/nq-training.jsonl"
							lowercase_          =    "data/nq-validation.jsonl"







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


														"""simple docstring"""


														os.makedirs(self.base_dir							,      exist_ok=lowerCAmelCase_ )
														_a     					=    os.path.join(self.base_dir							,      self.save_dir )
														_a     					=    self.batch_size_per_device * jax.device_count()



@dataclass
class       A   :
							lowercase_          =    42
							lowercase_          =    4096  # no dynamic padding on TPUs







							def __call__(  self	:  str							,      lowerCAmelCase_	:  Union[str, Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    self.collate_fn(lowerCAmelCase_ )
														_a     					=    jax.tree_util.tree_map(lowerCAmelCase_							,      lowerCAmelCase_ )
														return batch







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  List[Any] )      ->							int:


														"""simple docstring"""


														_a					,							_a     					=    self.fetch_inputs(features['''input_ids'''] )
														_a     					=    {
														    '''input_ids''': jnp.array(lowerCAmelCase_							,      dtype=jnp.intaa ),
														    '''attention_mask''': jnp.array(lowerCAmelCase_							,      dtype=jnp.intaa ),
														    '''start_labels''': jnp.array(features['''start_token''']							,      dtype=jnp.intaa ),
														    '''end_labels''': jnp.array(features['''end_token''']							,      dtype=jnp.intaa ),
														    '''pooled_labels''': jnp.array(features['''category''']							,      dtype=jnp.intaa ),
														}
														return batch







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  list )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    [self._fetch_inputs(lowerCAmelCase_ ) for ids in input_ids]
														return zip(*lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  list )      ->							str:


														"""simple docstring"""


														_a     					=    [1 for _ in range(len(lowerCAmelCase_ ) )]
														while len(lowerCAmelCase_ ) < self.max_length:
																					input_ids.append(self.pad_id )
																					attention_mask.append(0 )
														return input_ids, attention_mask



def 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Dict=None				):





							'''simple docstring'''


							if seed is not None:
														_a     					=    dataset.shuffle(seed=UpperCamelCase				)
							for i in range(len(UpperCamelCase				) // batch_size				):
														_a     					=    dataset[i * batch_size : (i + 1) * batch_size]
														yield dict(UpperCamelCase				)
@partial(jax.pmap			,						axis_name='''batch'''				)
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Tuple			,						**UpperCamelCase	:  str				):





							'''simple docstring'''


							def loss_fn(UpperCamelCase	:  List[str]				):
														_a     					=    model_inputs.pop('''start_labels'''				)
														_a     					=    model_inputs.pop('''end_labels'''				)
														_a     					=    model_inputs.pop('''pooled_labels'''				)

														_a     					=    state.apply_fn(**UpperCamelCase			,						params=UpperCamelCase			,						dropout_rng=UpperCamelCase			,						train=UpperCamelCase				)
														_a					,							_a					,							_a     					=    outputs

														return state.loss_fn(
														    UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						)

							_a					,							_a     					=    jax.random.split(UpperCamelCase				)
							_a     					=    jax.value_and_grad(UpperCamelCase				)
							_a					,							_a     					=    grad_fn(state.params				)
							_a     					=    jax.lax.pmean({'''loss''': loss}			,						axis_name='''batch'''				)
							_a     					=    jax.lax.pmean(UpperCamelCase			,						'''batch'''				)

							_a     					=    state.apply_gradients(grads=UpperCamelCase				)
							return state, metrics, new_drp_rng
@partial(jax.pmap			,						axis_name='''batch'''				)
def 		snake_case_   (UpperCamelCase	:  Any			,						**UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    model_inputs.pop('''start_labels'''				)
							_a     					=    model_inputs.pop('''end_labels'''				)
							_a     					=    model_inputs.pop('''pooled_labels'''				)

							_a     					=    state.apply_fn(**UpperCamelCase			,						params=state.params			,						train=UpperCamelCase				)
							_a					,							_a					,							_a     					=    outputs

							_a     					=    state.loss_fn(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				)
							_a     					=    jax.lax.pmean({'''loss''': loss}			,						axis_name='''batch'''				)
							return metrics

class       A   (		train_state.TrainState   ):
							lowercase_          =    struct.field(pytree_node=_a   )



@dataclass
class       A   :
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    None







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


														"""simple docstring"""


														_a     					=    model.params
														_a     					=    TrainState.create(
														    apply_fn=model.__call__							,      params=lowerCAmelCase_							,      tx=lowerCAmelCase_							,      loss_fn=lowerCAmelCase_							,      )
														if ckpt_dir is not None:
																					_a					,							_a					,							_a					,							_a					,							_a     					=    restore_checkpoint(lowerCAmelCase_							,      lowerCAmelCase_ )
																					_a     					=    {
																					    '''lr''': args.lr,
																					    '''init_lr''': args.init_lr,
																					    '''warmup_steps''': args.warmup_steps,
																					    '''num_train_steps''': num_train_steps,
																					    '''weight_decay''': args.weight_decay,
																					}
																					_a					,							_a     					=    build_tx(**lowerCAmelCase_ )
																					_a     					=    train_state.TrainState(
																					    step=lowerCAmelCase_							,      apply_fn=model.__call__							,      params=lowerCAmelCase_							,      tx=lowerCAmelCase_							,      opt_state=lowerCAmelCase_							,      )
																					_a     					=    args
																					_a     					=    data_collator
																					_a     					=    lr
																					_a     					=    params
														_a     					=    jax_utils.replicate(lowerCAmelCase_ )
														return state







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Any							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  Optional[Any] )      ->							int:


														"""simple docstring"""


														_a     					=    self.args
														_a     					=    len(lowerCAmelCase_ ) // args.batch_size

														_a     					=    jax.random.PRNGKey(0 )
														_a     					=    jax.random.split(lowerCAmelCase_							,      jax.device_count() )
														for epoch in range(args.max_epochs ):
																					_a     					=    jnp.array(0							,      dtype=jnp.floataa )
																					_a     					=    get_batched_dataset(lowerCAmelCase_							,      args.batch_size							,      seed=lowerCAmelCase_ )
																					_a     					=    0
																					for batch in tqdm(lowerCAmelCase_							,      total=lowerCAmelCase_							,      desc=F'Running EPOCH-{epoch}' ):
																												_a     					=    self.data_collator(lowerCAmelCase_ )
																												_a					,							_a					,							_a     					=    self.train_step_fn(lowerCAmelCase_							,      lowerCAmelCase_							,      **lowerCAmelCase_ )
																												running_loss += jax_utils.unreplicate(metrics['''loss'''] )
																												i += 1
																												if i % args.logging_steps == 0:
																																			_a     					=    jax_utils.unreplicate(state.step )
																																			_a     					=    running_loss.item() / i
																																			_a     					=    self.scheduler_fn(state_step - 1 )

																																			_a     					=    self.evaluate(lowerCAmelCase_							,      lowerCAmelCase_ )
																																			_a     					=    {
																																			    '''step''': state_step.item(),
																																			    '''eval_loss''': eval_loss.item(),
																																			    '''tr_loss''': tr_loss,
																																			    '''lr''': lr.item(),
																																			}
																																			tqdm.write(str(lowerCAmelCase_ ) )
																																			self.logger.log(lowerCAmelCase_							,      commit=lowerCAmelCase_ )

																												if i % args.save_steps == 0:
																																			self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}'							,      state=lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  str )      ->							List[str]:


														"""simple docstring"""


														_a     					=    get_batched_dataset(lowerCAmelCase_							,      self.args.batch_size )
														_a     					=    len(lowerCAmelCase_ ) // self.args.batch_size
														_a     					=    jnp.array(0							,      dtype=jnp.floataa )
														_a     					=    0
														for batch in tqdm(lowerCAmelCase_							,      total=lowerCAmelCase_							,      desc='''Evaluating ... ''' ):
																					_a     					=    self.data_collator(lowerCAmelCase_ )
																					_a     					=    self.val_step_fn(lowerCAmelCase_							,      **lowerCAmelCase_ )
																					running_loss += jax_utils.unreplicate(metrics['''loss'''] )
																					i += 1
														return running_loss / i







							def        __lowerCAmelCase      (  self	:  int							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  str )      ->							int:


														"""simple docstring"""


														_a     					=    jax_utils.unreplicate(lowerCAmelCase_ )
														print(F'SAVING CHECKPOINT IN {save_dir}'							,      end=''' ... ''' )
														self.model_save_fn(lowerCAmelCase_							,      params=state.params )
														with open(os.path.join(lowerCAmelCase_							,      '''opt_state.msgpack''' )							,      '''wb''' ) as f:
																					f.write(to_bytes(state.opt_state ) )
														joblib.dump(self.args							,      os.path.join(lowerCAmelCase_							,      '''args.joblib''' ) )
														joblib.dump(self.data_collator							,      os.path.join(lowerCAmelCase_							,      '''data_collator.joblib''' ) )
														with open(os.path.join(lowerCAmelCase_							,      '''training_state.json''' )							,      '''w''' ) as f:
																					json.dump({'''step''': state.step.item()}							,      lowerCAmelCase_ )
														print('''DONE''' )



def 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  Dict				):





							'''simple docstring'''


							print(f'RESTORING CHECKPOINT FROM {save_dir}'			,						end=''' ... '''				)
							with open(os.path.join(UpperCamelCase			,						'''flax_model.msgpack'''				)			,						'''rb'''				) as f:
														_a     					=    from_bytes(state.params			,						f.read()				)

							with open(os.path.join(UpperCamelCase			,						'''opt_state.msgpack'''				)			,						'''rb'''				) as f:
														_a     					=    from_bytes(state.opt_state			,						f.read()				)

							_a     					=    joblib.load(os.path.join(UpperCamelCase			,						'''args.joblib'''				)				)
							_a     					=    joblib.load(os.path.join(UpperCamelCase			,						'''data_collator.joblib'''				)				)

							with open(os.path.join(UpperCamelCase			,						'''training_state.json'''				)			,						'''r'''				) as f:
														_a     					=    json.load(UpperCamelCase				)
							_a     					=    training_state['''step''']

							print('''DONE'''				)
							return params, opt_state, step, args, data_collator
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							_a     					=    num_train_steps - warmup_steps
							_a     					=    optax.linear_schedule(init_value=UpperCamelCase			,						end_value=UpperCamelCase			,						transition_steps=UpperCamelCase				)
							_a     					=    optax.linear_schedule(init_value=UpperCamelCase			,						end_value=1e-7			,						transition_steps=UpperCamelCase				)
							_a     					=    optax.join_schedules(schedules=[warmup_fn, decay_fn]			,						boundaries=[warmup_steps]				)
							return lr

def 		snake_case_   (UpperCamelCase	:  Tuple			,						UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  str			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							def weight_decay_mask(UpperCamelCase	:  Dict				):
														_a     					=    traverse_util.flatten_dict(UpperCamelCase				)
														_a     					=    {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
														return traverse_util.unflatten_dict(UpperCamelCase				)

							_a     					=    scheduler_fn(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				)

							_a     					=    optax.adamw(learning_rate=UpperCamelCase			,						weight_decay=UpperCamelCase			,						mask=UpperCamelCase				)
							return tx, lr

