





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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''
	lowercase_		=      42
	lowercase_		=      jnp.floataa
	lowercase_		=      True

	def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->Dict:



			'''simple docstring'''



			super().setup()
			lowerCamelCase__:				Any        =nn.Dense(5	,			dtype=self.dtype)







	def __call__(self    :       Tuple	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       int)	->Optional[Any]:



			'''simple docstring'''



			lowerCamelCase__:				Optional[int]        =super().__call__(*UpperCAmelCase_	,			**UpperCAmelCase_)
			lowerCamelCase__:				Optional[Any]        =self.cls(outputs[2])
			return outputs[:2] + (cls_out,)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







	'''simple docstring'''
	lowercase_		=      FlaxBigBirdForNaturalQuestionsModule





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a		)					->		int:







		"""simple docstring"""





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

		lowerCamelCase__:				str        =partial(__a     ,   reduction=jnp.mean		)
		lowerCamelCase__:				Optional[Any]        =cross_entropy(__a     ,   __a		)
		lowerCamelCase__:				List[str]        =cross_entropy(__a     ,   __a		)
		lowerCamelCase__:				str        =cross_entropy(__a     ,   __a		)
		return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class        _SCREAMING_SNAKE_CASE						:







	'''simple docstring'''
	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 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



			'''simple docstring'''



			os.makedirs(self.base_dir	,			exist_ok=UpperCAmelCase_)
			lowerCamelCase__:				List[Any]        =os.path.join(self.base_dir	,			self.save_dir)
			lowerCamelCase__:				Tuple        =self.batch_size_per_device * jax.device_count()



@dataclass
class        _SCREAMING_SNAKE_CASE						:







	'''simple docstring'''
	lowercase_		=      42
	lowercase_		=      4096  # no dynamic padding on TPUs

	def __call__(self    :       str	,			UpperCAmelCase_    :       List[str])	->Dict:



			'''simple docstring'''



			lowerCamelCase__:				Tuple        =self.collate_fn(UpperCAmelCase_)
			lowerCamelCase__:				Optional[Any]        =jax.tree_util.tree_map(UpperCAmelCase_	,			UpperCAmelCase_)
			return batch

	def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			UpperCAmelCase_    :       Union[str, Any])	->List[str]:



			'''simple docstring'''



			lowerCamelCase__:				List[str]        =self.fetch_inputs(features["input_ids"])
			lowerCamelCase__:				Dict        ={
			    "input_ids": jnp.array(UpperCAmelCase_	,			dtype=jnp.intaa),
			    "attention_mask": jnp.array(UpperCAmelCase_	,			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    :       List[str]	,			UpperCAmelCase_    :       list)	->Dict:



			'''simple docstring'''



			lowerCamelCase__:				Dict        =[self._fetch_inputs(UpperCAmelCase_) for ids in input_ids]
			return zip(*UpperCAmelCase_)







	def 							SCREAMING_SNAKE_CASE_ (self    :       List[str]	,			UpperCAmelCase_    :       list)	->List[str]:



			'''simple docstring'''



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





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a=None		)					->		int:







		"""simple docstring"""





		if seed is not None:
				lowerCamelCase__:				Dict        =dataset.shuffle(seed=__a		)
		for i in range(len(__a		) // batch_size		):
				lowerCamelCase__:				Optional[Any]        =dataset[i * batch_size : (i + 1) * batch_size]
				yield dict(__a		)





@partial(jax.pmap     ,   axis_name="batch"		)
def 					lowerCAmelCase_			(	__a     ,   __a     ,   **__a		)					->		Optional[Any]:







		"""simple docstring"""





		def loss_fn(__a		):
				lowerCamelCase__:				Dict        =model_inputs.pop("start_labels"		)
				lowerCamelCase__:				str        =model_inputs.pop("end_labels"		)
				lowerCamelCase__:				Optional[int]        =model_inputs.pop("pooled_labels"		)

				lowerCamelCase__:				str        =state.apply_fn(**__a     ,   params=__a     ,   dropout_rng=__a     ,   train=__a		)
				lowerCamelCase__:				Dict        =outputs

				return state.loss_fn(
				    __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a     ,   )

		lowerCamelCase__:				str        =jax.random.split(__a		)
		lowerCamelCase__:				List[str]        =jax.value_and_grad(__a		)
		lowerCamelCase__:				List[Any]        =grad_fn(state.params		)
		lowerCamelCase__:				Any        =jax.lax.pmean({"loss": loss}     ,   axis_name="batch"		)
		lowerCamelCase__:				Dict        =jax.lax.pmean(__a     ,   "batch"		)

		lowerCamelCase__:				Optional[Any]        =state.apply_gradients(grads=__a		)
		return state, metrics, new_drp_rng





@partial(jax.pmap     ,   axis_name="batch"		)
def 					lowerCAmelCase_			(	__a     ,   **__a		)					->		int:







		"""simple docstring"""





		lowerCamelCase__:				Union[str, Any]        =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(**__a     ,   params=state.params     ,   train=__a		)
		lowerCamelCase__:				str        =outputs

		lowerCamelCase__:				str        =state.loss_fn(__a     ,   __a     ,   __a     ,   __a     ,   __a     ,   __a		)
		lowerCamelCase__:				Tuple        =jax.lax.pmean({"loss": loss}     ,   axis_name="batch"		)
		return metrics



class        _SCREAMING_SNAKE_CASE						(     train_state.TrainState   ):







	'''simple docstring'''
	lowercase_		=      struct.field(pytree_node=__SCREAMING_SNAKE_CASE   )



@dataclass
class        _SCREAMING_SNAKE_CASE						:







	'''simple docstring'''
	lowercase_		=      42
	lowercase_		=      42
	lowercase_		=      42
	lowercase_		=      42
	lowercase_		=      42
	lowercase_		=      42
	lowercase_		=      None

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



			'''simple docstring'''



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

	def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int]	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       List[str]	,			UpperCAmelCase_    :       Dict)	->List[Any]:



			'''simple docstring'''



			lowerCamelCase__:				Union[str, Any]        =self.args
			lowerCamelCase__:				Dict        =len(UpperCAmelCase_) // args.batch_size

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

									lowerCamelCase__:				List[Any]        =self.evaluate(UpperCAmelCase_	,			UpperCAmelCase_)
									lowerCamelCase__:				Dict        ={
									    "step": state_step.item(),
									    "eval_loss": eval_loss.item(),
									    "tr_loss": tr_loss,
									    "lr": lr.item(),
									}
									tqdm.write(str(UpperCAmelCase_))
									self.logger.log(UpperCAmelCase_	,			commit=UpperCAmelCase_)

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

	def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Union[str, Any])	->Dict:



			'''simple docstring'''



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







	def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       str)	->str:



			'''simple docstring'''



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





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Any:







		"""simple docstring"""





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

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

		lowerCamelCase__:				str        =joblib.load(os.path.join(__a     ,   "args.joblib"		)		)
		lowerCamelCase__:				Tuple        =joblib.load(os.path.join(__a     ,   "data_collator.joblib"		)		)

		with open(os.path.join(__a     ,   "training_state.json"		)     ,   "r"		) as f:
				lowerCamelCase__:				List[str]        =json.load(__a		)
		lowerCamelCase__:				List[Any]        =training_state["step"]

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





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a		)					->		str:







		"""simple docstring"""





		lowerCamelCase__:				Tuple        =num_train_steps - warmup_steps
		lowerCamelCase__:				int        =optax.linear_schedule(init_value=__a     ,   end_value=__a     ,   transition_steps=__a		)
		lowerCamelCase__:				Union[str, Any]        =optax.linear_schedule(init_value=__a     ,   end_value=1e-7     ,   transition_steps=__a		)
		lowerCamelCase__:				Dict        =optax.join_schedules(schedules=[warmup_fn, decay_fn]     ,   boundaries=[warmup_steps]		)
		return lr






def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a     ,   __a     ,   __a		)					->		Optional[int]:







		"""simple docstring"""





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

		lowerCamelCase__:				Optional[int]        =scheduler_fn(__a     ,   __a     ,   __a     ,   __a		)

		lowerCamelCase__:				str        =optax.adamw(learning_rate=__a     ,   weight_decay=__a     ,   mask=__a		)
		return tx, lr








import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__A     =    "src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__A     =    direct_transformers_import(PATH_TO_TRANSFORMERS)

__A     =    transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__A     =    re.compile(R"\[(.+?)\]\((https://huggingface\.co/.+?)\)")


__A     =    {
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
    "MusicgenConfig",
    "RagConfig",
    "SpeechEncoderDecoderConfig",
    "TimmBackboneConfig",
    "VisionEncoderDecoderConfig",
    "VisionTextDualEncoderConfig",
    "LlamaConfig",
}





def 					lowerCAmelCase_			(	__a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				List[Any]        =None

  # source code of `config_class`
  lowerCamelCase__:				List[Any]        =inspect.getsource(__a		)
  lowerCamelCase__:				str        =_re_checkpoint.findall(__a		)

  # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
  # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
  for ckpt_name, ckpt_link in checkpoints:
    # allow the link to end with `/`
    if ckpt_link.endswith("/"		):
      lowerCamelCase__:				str        =ckpt_link[:-1]

    # verify the checkpoint name corresponds to the checkpoint link
    lowerCamelCase__:				Optional[int]        =F"""https://huggingface.co/{ckpt_name}"""
    if ckpt_link == ckpt_link_from_name:
      lowerCamelCase__:				List[Any]        =ckpt_name
      break

  return checkpoint





def 					lowerCAmelCase_			(	)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[]

  for config_class in list(CONFIG_MAPPING.values()		):
    # Skip deprecated models
    if "models.deprecated" in config_class.__module__:
      continue
    lowerCamelCase__:				List[Any]        =get_checkpoint_from_config_class(__a		)

    lowerCamelCase__:				Tuple        =config_class.__name__
    if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
      configs_without_checkpoint.append(__a		)

  if len(__a		) > 0:
    lowerCamelCase__:				List[str]        ="\n".join(sorted(__a		)		)
    raise ValueError(F"""The following configurations don't contain any valid checkpoint:\n{message}"""		)


if __name__ == "__main__":
     check_config_docstrings_have_checkpoints()

