
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCamelCase_   	=	datasets.utils.logging.get_logger(__name__)

@dataclass
class       a		(   datasets.BuilderConfig					):
							lowercase_		:						Optional[datasets.Features]   							=  None
							lowercase_		:						str   							=  "utf-8"
							lowercase_		:						Optional[str]   							=  None
							lowercase_		:						Optional[str]   							=  None
							lowercase_		:						bool   							=  True  # deprecated
							lowercase_		:						Optional[int]   							=  None  # deprecated
							lowercase_		:						int   							=  10 << 20  # 10MB
							lowercase_		:						Optional[bool]   							=  None

class       a		(   datasets.ArrowBasedBuilder					):
							lowercase_		:						Union[str, Any]   							=  JsonConfig







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								if self.config.block_size is not None:
									logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead" )
									__lowerCAmelCase         =							self.config.block_size
								if self.config.use_threads is not True:
									logger.warning(
									    "The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore." )
								if self.config.newlines_in_values is not None:
									raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported" )
								return datasets.DatasetInfo(features=self.config.features )







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  List[Any] ):
								"""simple docstring"""







								if not self.config.data_files:
									raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}" )
								__lowerCAmelCase         =							dl_manager.download_and_extract(self.config.data_files )
								if isinstance(snake_case__      ,				(str, list, tuple) ):
									__lowerCAmelCase         =							data_files
									if isinstance(snake_case__      ,				snake_case__ ):
										__lowerCAmelCase         =							[files]
									__lowerCAmelCase         =							[dl_manager.iter_files(snake_case__ ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,				gen_kwargs={"files": files} )]
								__lowerCAmelCase         =							[]
								for split_name, files in data_files.items():
									if isinstance(snake_case__      ,				snake_case__ ):
										__lowerCAmelCase         =							[files]
									__lowerCAmelCase         =							[dl_manager.iter_files(snake_case__ ) for file in files]
									splits.append(datasets.SplitGenerator(name=snake_case__      ,				gen_kwargs={"files": files} ) )
								return splits







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  pa.Table ):
								"""simple docstring"""







								if self.config.features is not None:
									# adding missing columns
									for column_name in set(self.config.features ) - set(pa_table.column_names ):
										__lowerCAmelCase         =							self.config.features.arrow_schema.field(snake_case__ ).type
										__lowerCAmelCase         =							pa_table.append_column(snake_case__      ,				pa.array([None] * len(snake_case__ )      ,				type=snake_case__ ) )
									# more expensive cast to support nested structures with keys in a different order
									# allows str <-> int/float or str to Audio for example
									__lowerCAmelCase         =							table_cast(snake_case__      ,				self.config.features.arrow_schema )
								return pa_table







							def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
									# If the file is one json object and if we need to look at the list of items in one specific field
									if self.config.field is not None:
										with open(snake_case__      ,				encoding=self.config.encoding      ,				errors=self.config.encoding_errors ) as f:
											__lowerCAmelCase         =							json.load(snake_case__ )

										# We keep only the field we are interested in
										__lowerCAmelCase         =							dataset[self.config.field]

										# We accept two format: a list of dicts or a dict of lists
										if isinstance(snake_case__      ,				(list, tuple) ):
											__lowerCAmelCase         =							set().union(*[row.keys() for row in dataset] )
											__lowerCAmelCase         =							{col: [row.get(snake_case__ ) for row in dataset] for col in keys}
										else:
											__lowerCAmelCase         =							dataset
										__lowerCAmelCase         =							pa.Table.from_pydict(snake_case__ )
										yield file_idx, self._cast_table(snake_case__ )

									# If the file has one json object per line
									else:
										with open(snake_case__      ,				"rb" ) as f:
											__lowerCAmelCase         =							0
											# Use block_size equal to the chunk size divided by 32 to leverage multithreading
											# Set a default minimum value of 16kB if the chunk size is really small
											__lowerCAmelCase         =							max(self.config.chunksize // 32      ,				16 << 10 )
											__lowerCAmelCase         =							(
											    self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
											)
											while True:
												__lowerCAmelCase         =							f.read(self.config.chunksize )
												if not batch:
													break
												# Finish current line
												try:
													batch += f.readline()
												except (AttributeError, io.UnsupportedOperation):
													batch += readline(snake_case__ )
												# PyArrow only accepts utf-8 encoded bytes
												if self.config.encoding != "utf-8":
													__lowerCAmelCase         =							batch.decode(self.config.encoding      ,				errors=snake_case__ ).encode("utf-8" )
												try:
													while True:
														try:
															__lowerCAmelCase         =							paj.read_json(
															    io.BytesIO(snake_case__ )      ,				read_options=paj.ReadOptions(block_size=snake_case__ ) )
															break
														except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
															if (
															    isinstance(snake_case__      ,				pa.ArrowInvalid )
															    and "straddling" not in str(snake_case__ )
															    or block_size > len(snake_case__ )
															):
																raise
															else:
																# Increase the block size in case it was too small.
																# The block size will be reset for the next file.
																logger.debug(
																    F"Batch of {len(snake_case__ )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}." )
																block_size *= 2
												except pa.ArrowInvalid as e:
													try:
														with open(
														    snake_case__      ,				encoding=self.config.encoding      ,				errors=self.config.encoding_errors ) as f:
															__lowerCAmelCase         =							json.load(snake_case__ )
													except json.JSONDecodeError:
														logger.error(F"Failed to read file '{file}' with error {type(snake_case__ )}: {e}" )
														raise e
													# If possible, parse the file as a list of json objects and exit the loop
													if isinstance(snake_case__      ,				snake_case__ ):  # list is the only sequence type supported in JSON
														try:
															__lowerCAmelCase         =							set().union(*[row.keys() for row in dataset] )
															__lowerCAmelCase         =							{col: [row.get(snake_case__ ) for row in dataset] for col in keys}
															__lowerCAmelCase         =							pa.Table.from_pydict(snake_case__ )
														except (pa.ArrowInvalid, AttributeError) as e:
															logger.error(F"Failed to read file '{file}' with error {type(snake_case__ )}: {e}" )
															raise ValueError(F"Not able to read records in the JSON file at {file}." ) from None
														yield file_idx, self._cast_table(snake_case__ )
														break
													else:
														logger.error(F"Failed to read file '{file}' with error {type(snake_case__ )}: {e}" )
														raise ValueError(
														    F"Not able to read records in the JSON file at {file}. "
														    F"You should probably indicate the field of the JSON file containing your records. "
														    F"This JSON file contain the following fields: {str(list(dataset.keys() ) )}. "
														    F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. " ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
												yield (file_idx, batch_idx), self._cast_table(snake_case__ )
												batch_idx += 1








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		(   __UpperCAmelCase					):
							lowercase_		:						BigBirdConfig
							lowercase_		:						jnp.dtype   							=  jnp.floataa
							lowercase_		:						bool   							=  True







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







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







							def __call__(						self  :  Optional[Any]      ,				*snake_case__  :  List[str]      ,				**snake_case__  :  str ):
								"""simple docstring"""







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

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  FlaxBigBirdForNaturalQuestionsModule






def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[str]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[Any]			,							UpperCamelCase:     int  ):






	"""simple docstring"""


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

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

@dataclass
class       a		:
							lowercase_		:						str   							=  "google/bigbird-roberta-base"
							lowercase_		:						int   							=  3_000
							lowercase_		:						int   							=  10_500

							lowercase_		:						int   							=  128
							lowercase_		:						int   							=  3

							lowercase_		:						int   							=  1
							lowercase_		:						int   							=  5

							# tx_args
							lowercase_		:						float   							=  3e-5
							lowercase_		:						float   							=  0.0
							lowercase_		:						int   							=  20_000
							lowercase_		:						float   							=  0.0095

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







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







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

@dataclass
class       a		:
							lowercase_		:						int
							lowercase_		:						int   							=  4_096  # no dynamic padding on TPUs







							def __call__(						self  :  List[Any]      ,				snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.collate_fn(snake_case__ )
								__lowerCAmelCase         =							jax.tree_util.tree_map(snake_case__      ,				snake_case__ )
								return batch







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.fetch_inputs(features["input_ids"] )
								__lowerCAmelCase         =							{
								    "input_ids": jnp.array(snake_case__      ,				dtype=jnp.intaa ),
								    "attention_mask": jnp.array(snake_case__      ,				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  :  Optional[int]      ,				snake_case__  :  list ):
								"""simple docstring"""







								__lowerCAmelCase         =							[self._fetch_inputs(snake_case__ ) for ids in input_ids]
								return zip(*snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  list ):
								"""simple docstring"""







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






def 		_UpperCAmelCase   (	UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Optional[Any]=None  ):






	"""simple docstring"""


	if seed is not None:
		__lowerCAmelCase         =							dataset.shuffle(seed=UpperCamelCase  )
	for i in range(len(UpperCamelCase  ) // batch_size  ):
		__lowerCAmelCase         =							dataset[i * batch_size : (i + 1) * batch_size]
		yield dict(UpperCamelCase  )






@partial(jax.pmap			,							axis_name="batch"  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Union[str, Any]			,							**UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	def loss_fn(UpperCamelCase:     Dict  ):
		__lowerCAmelCase         =							model_inputs.pop("start_labels"  )
		__lowerCAmelCase         =							model_inputs.pop("end_labels"  )
		__lowerCAmelCase         =							model_inputs.pop("pooled_labels"  )

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

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

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

	__lowerCAmelCase         =							state.apply_gradients(grads=UpperCamelCase  )
	return state, metrics, new_drp_rng






@partial(jax.pmap			,							axis_name="batch"  )
def 		_UpperCAmelCase   (	UpperCamelCase:     Optional[Any]			,							**UpperCamelCase:     List[str]  ):






	"""simple docstring"""


	__lowerCAmelCase         =							model_inputs.pop("start_labels"  )
	__lowerCAmelCase         =							model_inputs.pop("end_labels"  )
	__lowerCAmelCase         =							model_inputs.pop("pooled_labels"  )

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

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

class       a		(   train_state.TrainState					):
							lowercase_		:						Callable   							=  struct.field(pytree_node=__UpperCAmelCase					)

@dataclass
class       a		:
							lowercase_		:						Args
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						Callable
							lowercase_		:						wandb
							lowercase_		:						Callable   							=  None







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  int      ,				snake_case__  :  List[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  str=None ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  int      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.args
								__lowerCAmelCase         =							len(snake_case__ ) // args.batch_size

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

											__lowerCAmelCase         =							self.evaluate(snake_case__      ,				snake_case__ )
											__lowerCAmelCase         =							{
											    "step": state_step.item(),
											    "eval_loss": eval_loss.item(),
											    "tr_loss": tr_loss,
											    "lr": lr.item(),
											}
											tqdm.write(str(snake_case__ ) )
											self.logger.log(snake_case__      ,				commit=snake_case__ )

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







							def       UpperCAmelCase__					(						self  :  List[Any]      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Optional[int]      ,				snake_case__  :  Any ):
								"""simple docstring"""







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






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     List[Any]  ):






	"""simple docstring"""


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

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

	__lowerCAmelCase         =							joblib.load(os.path.join(UpperCamelCase			,							"args.joblib"  )  )
	__lowerCAmelCase         =							joblib.load(os.path.join(UpperCamelCase			,							"data_collator.joblib"  )  )

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

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






def 		_UpperCAmelCase   (	UpperCamelCase:     Any			,							UpperCamelCase:     Dict			,							UpperCamelCase:     Tuple			,							UpperCamelCase:     Dict  ):






	"""simple docstring"""


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







def 		_UpperCAmelCase   (	UpperCamelCase:     Union[str, Any]			,							UpperCamelCase:     str			,							UpperCamelCase:     List[Any]			,							UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Union[str, Any]  ):






	"""simple docstring"""


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

	__lowerCAmelCase         =							scheduler_fn(UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase  )

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






