





"""simple docstring"""



import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


a_ =			logging.getLogger(__name__)
torch.set_grad_enabled(False)
a_ =			"""cuda""" if torch.cuda.is_available() else """cpu"""







def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Tuple=100				,snake_case_  :  int=" "						)   ->List[str]:



    '''simple docstring'''







    __A    :						Dict						 =     text.split(snake_case_						)
    return [character.join(text[i : i + n]						).strip() for i in range(0				,len(snake_case_						)				,snake_case_						)]







def 		__lowercase     (   snake_case_  :  dict						)   ->dict:



    '''simple docstring'''







    __A ,       __A    :						Optional[int]						 =     [], []
    for title, text in zip(documents['''title''']				,documents['''text''']						):
        if text is not None:
            for passage in split_text(snake_case_						):
                titles.append(title if title is not None else ''''''						)
                texts.append(snake_case_						)
    return {"title": titles, "text": texts}







def 		__lowercase     (   snake_case_  :  dict				,snake_case_  :  DPRContextEncoder				,snake_case_  :  DPRContextEncoderTokenizerFast						)   ->dict:



    '''simple docstring'''







    __A    :						Tuple						 =     ctx_tokenizer(
        documents['''title''']				,documents['''text''']				,truncation=snake_case_				,padding='''longest'''				,return_tensors='''pt'''						)['''input_ids''']
    __A    :						int						 =     ctx_encoder(input_ids.to(device=snake_case_						)				,return_dict=snake_case_						).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}







def 		__lowercase     (   snake_case_  :  "RagExampleArguments"				,snake_case_  :  "ProcessingArguments"				,snake_case_  :  "IndexHnswArguments"				,)   ->Tuple:



    '''simple docstring'''







    logger.info('''Step 1 - Create the dataset'''						)
    ######################################

    # The dataset needed for RAG must have three columns:
    # - title (string): title of the document
    # - text (string): text of a passage of the document
    # - embeddings (array of dimension d): DPR representation of the passage
    # Let's say you have documents in tab-separated csv files with columns "title" and "text"
    assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

    # You can load a Dataset object this way
    __A    :						Optional[Any]						 =     load_dataset(
        '''csv'''				,data_files=[rag_example_args.csv_path]				,split='''train'''				,delimiter='''\t'''				,column_names=['''title''', '''text''']						)

    # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

    # Then split the documents into passages of 100 words
    __A    :						str						 =     dataset.map(snake_case_				,batched=snake_case_				,num_proc=processing_args.num_proc						)

    # And compute the embeddings
    __A    :						Union[str, Any]						 =     DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=snake_case_						)
    __A    :						str						 =     DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
    __A    :						Optional[int]						 =     Features(
        {'''text''': Value('''string'''						), '''title''': Value('''string'''						), '''embeddings''': Sequence(Value('''float32'''						)						)}						)  # optional, save as float32 instead of float64 to save space
    __A    :						List[Any]						 =     dataset.map(
        partial(snake_case_				,ctx_encoder=snake_case_				,ctx_tokenizer=snake_case_						)				,batched=snake_case_				,batch_size=processing_args.batch_size				,features=snake_case_				,)

    # And finally save your dataset
    __A    :						int						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset'''						)
    dataset.save_to_disk(snake_case_						)
    # from datasets import load_from_disk
    # dataset = load_from_disk(passages_path)  # to reload the dataset

    ######################################
    logger.info('''Step 2 - Index the dataset'''						)
    ######################################

    # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
    __A    :						Tuple						 =     faiss.IndexHNSWFlat(index_hnsw_args.d				,index_hnsw_args.m				,faiss.METRIC_INNER_PRODUCT						)
    dataset.add_faiss_index('''embeddings'''				,custom_index=snake_case_						)

    # And save the index
    __A    :						List[str]						 =     os.path.join(rag_example_args.output_dir				,'''my_knowledge_dataset_hnsw_index.faiss'''						)
    dataset.get_index('''embeddings'''						).save(snake_case_						)
    # dataset.load_faiss_index("embeddings", index_path)  # to reload the index






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=str(Path(SCREAMING_SNAKE_CASE__     ).parent / """test_run""" / """dummy-kb""" / """my_knowledge_dataset.csv"""     )							,							metadata={"""help""": """Path to a tab-separated csv file with columns 'title' and 'text'"""}							,							)
 _lowerCamelCase        =     field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}							,							)
 _lowerCamelCase        =     field(
     default="""facebook/rag-sequence-nq"""							,							metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}							,							)
 _lowerCamelCase        =     field(
     default="""facebook/dpr-ctx_encoder-multiset-base"""							,							metadata={
         """help""": (
             """The DPR context encoder model to use. Either 'facebook/dpr-ctx_encoder-single-nq-base' or"""
             """ 'facebook/dpr-ctx_encoder-multiset-base'"""
         )
     }							,							)
 _lowerCamelCase        =     field(
     default=str(Path(SCREAMING_SNAKE_CASE__     ).parent / """test_run""" / """dummy-kb"""     )							,							metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}							,							)






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=SCREAMING_SNAKE_CASE__							,							metadata={
         """help""": """The number of processes to use to split the documents into passages. Default is single process."""
     }							,							)
 _lowerCamelCase        =     field(
     default=16							,							metadata={
         """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
     }							,							)






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     field(
     default=7_68							,							metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}							,							)
 _lowerCamelCase        =     field(
     default=1_28							,							metadata={
         """help""": (
             """The number of bi-directional links created for every new element during the HNSW index construction."""
         )
     }							,							)


if __name__ == "__main__":
       logging.basicConfig(level=logging.WARNING)
       logger.setLevel(logging.INFO)

       a_ =			HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
       a_ ,			a_ ,			a_ =			parser.parse_args_into_dataclasses()
       with TemporaryDirectory() as tmp_dir:
              a_ =			rag_example_args.output_dir or tmp_dir
              main(rag_example_args, processing_args, index_hnsw_args)











"""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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     jnp.floataa
 _lowerCamelCase        =     True

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().setup()
     __A    :						List[Any]						 =     nn.Dense(5					,						dtype=self.dtype  )







 def __call__(    self					,						*__lowerCamelCase					,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     super().__call__(*__lowerCamelCase					,						**__lowerCamelCase  )
     __A    :						Optional[int]						 =     self.cls(outputs[2]  )
     return outputs[:2] + (cls_out,)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     FlaxBigBirdForNaturalQuestionsModule







def 		__lowercase     (   snake_case_  :  Tuple				,snake_case_  :  Optional[int]				,snake_case_  :  int				,snake_case_  :  Tuple				,snake_case_  :  Any				,snake_case_  :  Any						)   ->List[Any]:



    '''simple docstring'''







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

    __A    :						str						 =     partial(snake_case_				,reduction=jnp.mean						)
    __A    :						Dict						 =     cross_entropy(snake_case_				,snake_case_						)
    __A    :						List[str]						 =     cross_entropy(snake_case_				,snake_case_						)
    __A    :						str						 =     cross_entropy(snake_case_				,snake_case_						)
    return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     "google/bigbird-roberta-base"
 _lowerCamelCase        =     30_00
 _lowerCamelCase        =     1_05_00

 _lowerCamelCase        =     1_28
 _lowerCamelCase        =     3

 _lowerCamelCase        =     1
 _lowerCamelCase        =     5

 # tx_args
 _lowerCamelCase        =     3e-5
 _lowerCamelCase        =     0.0
 _lowerCamelCase        =     2_00_00
 _lowerCamelCase        =     0.0_0_9_5

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     os.makedirs(self.base_dir					,						exist_ok=__lowerCamelCase  )
     __A    :						Dict						 =     os.path.join(self.base_dir					,						self.save_dir  )
     __A    :						Dict						 =     self.batch_size_per_device * jax.device_count()






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     40_96  # no dynamic padding on TPUs

 def __call__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.collate_fn(__lowerCamelCase  )
     __A    :						Tuple						 =     jax.tree_util.tree_map(__lowerCamelCase					,						__lowerCamelCase  )
     return batch

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A ,       __A    :						List[Any]						 =     self.fetch_inputs(features['''input_ids''']  )
     __A    :						Union[str, Any]						 =     {
         '''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 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     [self._fetch_inputs(__lowerCamelCase  ) for ids in input_ids]
     return zip(*__lowerCamelCase  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     [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 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Optional[Any]				,snake_case_  :  List[str]=None						)   ->Optional[int]:



    '''simple docstring'''







    if seed is not None:
        __A    :						List[Any]						 =     dataset.shuffle(seed=snake_case_						)
    for i in range(len(snake_case_						) // batch_size						):
        __A    :						Tuple						 =     dataset[i * batch_size : (i + 1) * batch_size]
        yield dict(snake_case_						)







@partial(jax.pmap				,axis_name='''batch'''						)
def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Union[str, Any]				,**snake_case_  :  List[str]						)   ->Tuple:



    '''simple docstring'''







    def loss_fn(snake_case_  :  List[str]						):
        __A    :						str						 =     model_inputs.pop('''start_labels'''						)
        __A    :						str						 =     model_inputs.pop('''end_labels'''						)
        __A    :						int						 =     model_inputs.pop('''pooled_labels'''						)

        __A    :						Dict						 =     state.apply_fn(**snake_case_				,params=snake_case_				,dropout_rng=snake_case_				,train=snake_case_						)
        __A ,       __A ,       __A    :						Union[str, Any]						 =     outputs

        return state.loss_fn(
            snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_				,)

    __A ,       __A    :						int						 =     jax.random.split(snake_case_						)
    __A    :						str						 =     jax.value_and_grad(snake_case_						)
    __A ,       __A    :						Optional[int]						 =     grad_fn(state.params						)
    __A    :						List[str]						 =     jax.lax.pmean({'''loss''': loss}				,axis_name='''batch'''						)
    __A    :						List[str]						 =     jax.lax.pmean(snake_case_				,'''batch'''						)

    __A    :						str						 =     state.apply_gradients(grads=snake_case_						)
    return state, metrics, new_drp_rng







@partial(jax.pmap				,axis_name='''batch'''						)
def 		__lowercase     (   snake_case_  :  int				,**snake_case_  :  Union[str, Any]						)   ->List[str]:



    '''simple docstring'''







    __A    :						Tuple						 =     model_inputs.pop('''start_labels'''						)
    __A    :						Dict						 =     model_inputs.pop('''end_labels'''						)
    __A    :						int						 =     model_inputs.pop('''pooled_labels'''						)

    __A    :						List[str]						 =     state.apply_fn(**snake_case_				,params=state.params				,train=snake_case_						)
    __A ,       __A ,       __A    :						Dict						 =     outputs

    __A    :						Optional[int]						 =     state.loss_fn(snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_						)
    __A    :						List[str]						 =     jax.lax.pmean({'''loss''': loss}				,axis_name='''batch'''						)
    return metrics






class 		__snake_case					(		train_state.TrainState     ):





 """simple docstring"""


 _lowerCamelCase        =     struct.field(pytree_node=SCREAMING_SNAKE_CASE__     )






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						Tuple						 =     model.params
     __A    :						Union[str, Any]						 =     TrainState.create(
         apply_fn=model.__call__					,						params=__lowerCamelCase					,						tx=__lowerCamelCase					,						loss_fn=__lowerCamelCase					,						)
     if ckpt_dir is not None:
         __A ,       __A ,       __A ,       __A ,       __A    :						Optional[Any]						 =     restore_checkpoint(__lowerCamelCase					,						__lowerCamelCase  )
         __A    :						List[Any]						 =     {
             '''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    :						List[str]						 =     build_tx(**__lowerCamelCase  )
         __A    :						int						 =     train_state.TrainState(
             step=__lowerCamelCase					,						apply_fn=model.__call__					,						params=__lowerCamelCase					,						tx=__lowerCamelCase					,						opt_state=__lowerCamelCase					,						)
         __A    :						int						 =     args
         __A    :						Optional[Any]						 =     data_collator
         __A    :						Tuple						 =     lr
         __A    :						List[Any]						 =     params
     __A    :						Dict						 =     jax_utils.replicate(__lowerCamelCase  )
     return state

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.args
     __A    :						Dict						 =     len(__lowerCamelCase  ) // args.batch_size

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

                 __A    :						Union[str, Any]						 =     self.evaluate(__lowerCamelCase					,						__lowerCamelCase  )
                 __A    :						Optional[Any]						 =     {
                     '''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 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     get_batched_dataset(__lowerCamelCase					,						self.args.batch_size  )
     __A    :						int						 =     len(__lowerCamelCase  ) // self.args.batch_size
     __A    :						Optional[Any]						 =     jnp.array(0					,						dtype=jnp.floataa  )
     __A    :						Dict						 =     0
     for batch in tqdm(__lowerCamelCase					,						total=__lowerCamelCase					,						desc='''Evaluating ... '''  ):
         __A    :						List[str]						 =     self.data_collator(__lowerCamelCase  )
         __A    :						Union[str, Any]						 =     self.val_step_fn(__lowerCamelCase					,						**__lowerCamelCase  )
         running_loss += jax_utils.unreplicate(metrics['''loss''']  )
         i += 1
     return running_loss / i







 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Dict						 =     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 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Dict						)   ->Optional[int]:



    '''simple docstring'''







    print(F"""RESTORING CHECKPOINT FROM {save_dir}"""				,end=''' ... '''						)
    with open(os.path.join(snake_case_				,'''flax_model.msgpack'''						)				,'''rb'''						) as f:
        __A    :						List[Any]						 =     from_bytes(state.params				,f.read()						)

    with open(os.path.join(snake_case_				,'''opt_state.msgpack'''						)				,'''rb'''						) as f:
        __A    :						Optional[int]						 =     from_bytes(state.opt_state				,f.read()						)

    __A    :						Tuple						 =     joblib.load(os.path.join(snake_case_				,'''args.joblib'''						)						)
    __A    :						List[str]						 =     joblib.load(os.path.join(snake_case_				,'''data_collator.joblib'''						)						)

    with open(os.path.join(snake_case_				,'''training_state.json'''						)				,'''r'''						) as f:
        __A    :						Dict						 =     json.load(snake_case_						)
    __A    :						int						 =     training_state['''step''']

    print('''DONE'''						)
    return params, opt_state, step, args, data_collator







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  Any				,snake_case_  :  Dict				,snake_case_  :  str						)   ->List[str]:



    '''simple docstring'''







    __A    :						str						 =     num_train_steps - warmup_steps
    __A    :						Union[str, Any]						 =     optax.linear_schedule(init_value=snake_case_				,end_value=snake_case_				,transition_steps=snake_case_						)
    __A    :						Optional[Any]						 =     optax.linear_schedule(init_value=snake_case_				,end_value=1e-7				,transition_steps=snake_case_						)
    __A    :						Any						 =     optax.join_schedules(schedules=[warmup_fn, decay_fn]				,boundaries=[warmup_steps]						)
    return lr







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  List[Any]				,snake_case_  :  Union[str, Any]				,snake_case_  :  List[Any]				,snake_case_  :  str						)   ->List[str]:



    '''simple docstring'''







    def weight_decay_mask(snake_case_  :  List[Any]						):
        __A    :						List[Any]						 =     traverse_util.flatten_dict(snake_case_						)
        __A    :						int						 =     {k: (v[-1] != '''bias''' and v[-2:] != ('''LayerNorm''', '''scale''')) for k, v in params.items()}
        return traverse_util.unflatten_dict(snake_case_						)

    __A    :						List[Any]						 =     scheduler_fn(snake_case_				,snake_case_				,snake_case_				,snake_case_						)

    __A    :						List[str]						 =     optax.adamw(learning_rate=snake_case_				,weight_decay=snake_case_				,mask=snake_case_						)
    return tx, lr




