



"""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__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      jnp.floataa
	SCREAMING_SNAKE_CASE        =      True

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Optional[Any]:

				"""simple docstring"""

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







	def __call__(	self:						Any			,     *_SCREAMING_SNAKE_CASE:						Optional[Any]			,     **_SCREAMING_SNAKE_CASE:						List[str])							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			super().__call__(*_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			self.cls(outputs[2])
				return outputs[:2] + (cls_out,)






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      FlaxBigBirdForNaturalQuestionsModule






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

			__lowerCAmelCase :    List[Any]				=			partial(__snake_case   ,reduction=jnp.mean      )
			__lowerCAmelCase :    int				=			cross_entropy(__snake_case   ,__snake_case      )
			__lowerCAmelCase :    Dict				=			cross_entropy(__snake_case   ,__snake_case      )
			__lowerCAmelCase :    str				=			cross_entropy(__snake_case   ,__snake_case      )
			return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class      A__  :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      "google/bigbird-roberta-base"
	SCREAMING_SNAKE_CASE        =      3_0_0_0
	SCREAMING_SNAKE_CASE        =      1_0_5_0_0

	SCREAMING_SNAKE_CASE        =      1_2_8
	SCREAMING_SNAKE_CASE        =      3

	SCREAMING_SNAKE_CASE        =      1
	SCREAMING_SNAKE_CASE        =      5

	# tx_args
	SCREAMING_SNAKE_CASE        =      3e-5
	SCREAMING_SNAKE_CASE        =      0.0
	SCREAMING_SNAKE_CASE        =      2_0_0_0_0
	SCREAMING_SNAKE_CASE        =      0.0_095

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

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

				"""simple docstring"""

				os.makedirs(self.base_dir			,     exist_ok=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			os.path.join(self.base_dir			,     self.save_dir)
				__lowerCAmelCase :    Dict				=			self.batch_size_per_device * jax.device_count()






@dataclass
class      A__  :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      4_0_9_6  # no dynamic padding on TPUs

	def __call__(	self:						str			,     _SCREAMING_SNAKE_CASE:						Dict)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.collate_fn(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			jax.tree_util.tree_map(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				return batch

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Any)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			self.fetch_inputs(features["input_ids"])
				__lowerCAmelCase :    List[Any]				=			{
				    "input_ids": jnp.array(_SCREAMING_SNAKE_CASE			,     dtype=jnp.intaa),
				    "attention_mask": jnp.array(_SCREAMING_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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						list)							->			str:

				"""simple docstring"""

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







	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     _SCREAMING_SNAKE_CASE:						list)							->			Dict:

				"""simple docstring"""

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






def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case=None      )		->  Dict:
			if seed is not None:
						__lowerCAmelCase :    List[Any]				=			dataset.shuffle(seed=__snake_case      )
			for i in range(len(__snake_case      ) // batch_size      ):
						__lowerCAmelCase :    Optional[Any]				=			dataset[i * batch_size : (i + 1) * batch_size]
						yield dict(__snake_case      )


@partial(jax.pmap   ,axis_name="batch"      )
def      _lowercase							(    __snake_case   ,__snake_case   ,**__snake_case      )		->  List[str]:
			def loss_fn(__snake_case      ):
						__lowerCAmelCase :    Union[str, Any]				=			model_inputs.pop("start_labels"      )
						__lowerCAmelCase :    Any				=			model_inputs.pop("end_labels"      )
						__lowerCAmelCase :    Dict				=			model_inputs.pop("pooled_labels"      )

						__lowerCAmelCase :    List[Any]				=			state.apply_fn(**__snake_case   ,params=__snake_case   ,dropout_rng=__snake_case   ,train=__snake_case      )
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			outputs

						return state.loss_fn(
						    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,)

			__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			jax.random.split(__snake_case      )
			__lowerCAmelCase :    Optional[int]				=			jax.value_and_grad(__snake_case      )
			__lowerCAmelCase		,      __lowerCAmelCase :    Optional[int]				=			grad_fn(state.params      )
			__lowerCAmelCase :    Union[str, Any]				=			jax.lax.pmean({"loss": loss}   ,axis_name="batch"      )
			__lowerCAmelCase :    Union[str, Any]				=			jax.lax.pmean(__snake_case   ,"batch"      )

			__lowerCAmelCase :    Dict				=			state.apply_gradients(grads=__snake_case      )
			return state, metrics, new_drp_rng


@partial(jax.pmap   ,axis_name="batch"      )
def      _lowercase							(    __snake_case   ,**__snake_case      )		->  List[str]:
			__lowerCAmelCase :    str				=			model_inputs.pop("start_labels"      )
			__lowerCAmelCase :    Any				=			model_inputs.pop("end_labels"      )
			__lowerCAmelCase :    Union[str, Any]				=			model_inputs.pop("pooled_labels"      )

			__lowerCAmelCase :    Tuple				=			state.apply_fn(**__snake_case   ,params=state.params   ,train=__snake_case      )
			__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[Any]				=			outputs

			__lowerCAmelCase :    Union[str, Any]				=			state.loss_fn(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )
			__lowerCAmelCase :    List[str]				=			jax.lax.pmean({"loss": loss}   ,axis_name="batch"      )
			return metrics



class      A__  (    train_state.TrainState				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      struct.field(pytree_node=__SCREAMING_SNAKE_CASE				)






@dataclass
class      A__  :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      42
	SCREAMING_SNAKE_CASE        =      None

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

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			self.args
				__lowerCAmelCase :    Optional[int]				=			len(_SCREAMING_SNAKE_CASE) // args.batch_size

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

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

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						str)							->			Optional[Any]:

				"""simple docstring"""

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







	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str])							->			List[str]:

				"""simple docstring"""

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






def      _lowercase							(    __snake_case   ,__snake_case      )		->  Tuple:
			print(F"""RESTORING CHECKPOINT FROM {save_dir}"""   ,end=" ... "      )
			with open(os.path.join(__snake_case   ,"flax_model.msgpack"      )   ,"rb"      ) as f:
						__lowerCAmelCase :    Any				=			from_bytes(state.params   ,f.read()      )

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

			__lowerCAmelCase :    Optional[int]				=			joblib.load(os.path.join(__snake_case   ,"args.joblib"      )      )
			__lowerCAmelCase :    List[Any]				=			joblib.load(os.path.join(__snake_case   ,"data_collator.joblib"      )      )

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

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


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Dict:
			__lowerCAmelCase :    Dict				=			num_train_steps - warmup_steps
			__lowerCAmelCase :    Any				=			optax.linear_schedule(init_value=__snake_case   ,end_value=__snake_case   ,transition_steps=__snake_case      )
			__lowerCAmelCase :    Any				=			optax.linear_schedule(init_value=__snake_case   ,end_value=1e-7   ,transition_steps=__snake_case      )
			__lowerCAmelCase :    Tuple				=			optax.join_schedules(schedules=[warmup_fn, decay_fn]   ,boundaries=[warmup_steps]      )
			return lr





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

			__lowerCAmelCase :    int				=			scheduler_fn(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )

			__lowerCAmelCase :    Tuple				=			optax.adamw(learning_rate=__snake_case   ,weight_decay=__snake_case   ,mask=__snake_case      )
			return tx, lr




"""simple docstring"""






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Tuple =       {
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xlnet'
	SCREAMING_SNAKE_CASE        =      ['mems']
	SCREAMING_SNAKE_CASE        =      {
	    'n_token': 'vocab_size',  # Backward compatibility
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}

	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str]=3_2000			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=1024			,     _SCREAMING_SNAKE_CASE:						Dict=24			,     _SCREAMING_SNAKE_CASE:						Optional[int]=16			,     _SCREAMING_SNAKE_CASE:						List[str]=4096			,     _SCREAMING_SNAKE_CASE:						Tuple="gelu"			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						List[Any]="bi"			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						List[str]=1e-12			,     _SCREAMING_SNAKE_CASE:						Any=0.1			,     _SCREAMING_SNAKE_CASE:						Any=512			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=None			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Any=False			,     _SCREAMING_SNAKE_CASE:						Tuple=False			,     _SCREAMING_SNAKE_CASE:						List[Any]=-1			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False			,     _SCREAMING_SNAKE_CASE:						Optional[int]="last"			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Dict="tanh"			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						Tuple=5			,     _SCREAMING_SNAKE_CASE:						List[str]=5			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=5			,     _SCREAMING_SNAKE_CASE:						Optional[int]=1			,     _SCREAMING_SNAKE_CASE:						Tuple=2			,     **_SCREAMING_SNAKE_CASE:						str			,     )							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			vocab_size
				__lowerCAmelCase :    Optional[int]				=			d_model
				__lowerCAmelCase :    Union[str, Any]				=			n_layer
				__lowerCAmelCase :    List[Any]				=			n_head
				if d_model % n_head != 0:
							raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0""")
				if "d_head" in kwargs:
							if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})""")
				__lowerCAmelCase :    Optional[Any]				=			d_model // n_head
				__lowerCAmelCase :    Tuple				=			ff_activation
				__lowerCAmelCase :    Union[str, Any]				=			d_inner
				__lowerCAmelCase :    Any				=			untie_r
				__lowerCAmelCase :    int				=			attn_type

				__lowerCAmelCase :    Any				=			initializer_range
				__lowerCAmelCase :    List[Any]				=			layer_norm_eps

				__lowerCAmelCase :    int				=			dropout
				__lowerCAmelCase :    List[Any]				=			mem_len
				__lowerCAmelCase :    str				=			reuse_len
				__lowerCAmelCase :    Any				=			bi_data
				__lowerCAmelCase :    Optional[int]				=			clamp_len
				__lowerCAmelCase :    Union[str, Any]				=			same_length

				__lowerCAmelCase :    List[str]				=			summary_type
				__lowerCAmelCase :    Optional[int]				=			summary_use_proj
				__lowerCAmelCase :    Tuple				=			summary_activation
				__lowerCAmelCase :    Dict				=			summary_last_dropout
				__lowerCAmelCase :    str				=			start_n_top
				__lowerCAmelCase :    List[str]				=			end_n_top

				__lowerCAmelCase :    str				=			bos_token_id
				__lowerCAmelCase :    Optional[Any]				=			pad_token_id
				__lowerCAmelCase :    Optional[Any]				=			eos_token_id

				if "use_cache" in kwargs:
							warnings.warn(
							    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
							    " instead."			,     _SCREAMING_SNAKE_CASE			,     )
							__lowerCAmelCase :    Optional[int]				=			kwargs["use_cache"]

				__lowerCAmelCase :    Optional[int]				=			use_mems_eval
				__lowerCAmelCase :    Any				=			use_mems_train
				super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			int:

				"""simple docstring"""

				logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")
				return -1







	@max_position_embeddings.setter
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						Optional[int])							->			Optional[int]:

				"""simple docstring"""

				raise NotImplementedError(
				    F"""The model {self.model_type} is one of the few models that has no sequence length limit.""")