




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 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  jnp.floataa
						lowerCAmelCase_							=  True






						def 			snake_case__		(		self				:	Union[str, Any]		):






							"""simple docstring"""






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






						def __call__(		self				:	List[Any]			,      *__lowercase				:	List[Any]			,      **__lowercase				:	Union[str, Any]		):






							"""simple docstring"""






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







class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  FlaxBigBirdForNaturalQuestionsModule







def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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

	snake_case_						       =	partial(_A	,      reduction=jnp.mean				)
	snake_case_						       =	cross_entropy(_A	,      _A				)
	snake_case_						       =	cross_entropy(_A	,      _A				)
	snake_case_						       =	cross_entropy(_A	,      _A				)
	return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  "google/bigbird-roberta-base"
						lowerCAmelCase_							=  3000
						lowerCAmelCase_							=  1_0500

						lowerCAmelCase_							=  128
						lowerCAmelCase_							=  3

						lowerCAmelCase_							=  1
						lowerCAmelCase_							=  5

						# tx_args
						lowerCAmelCase_							=  3E-5
						lowerCAmelCase_							=  0.0
						lowerCAmelCase_							=  2_0000
						lowerCAmelCase_							=  0.0095

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






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






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







@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  4096  # no dynamic padding on TPUs






						def __call__(		self				:	Union[str, Any]			,      __lowercase				:	Tuple		):






							"""simple docstring"""






							snake_case_						       =	self.collate_fn(__lowercase		)
							snake_case_						       =	jax.tree_util.tree_map(__lowercase			,      __lowercase		)
							return batch






						def 			snake_case__		(		self				:	List[Any]			,      __lowercase				:	int		):






							"""simple docstring"""






							snake_case_       , snake_case_						       =	self.fetch_inputs(features["input_ids"]		)
							snake_case_						       =	{
							    "input_ids": jnp.array(__lowercase			,      dtype=jnp.intaa		),
							    "attention_mask": jnp.array(__lowercase			,      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 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	list		):






							"""simple docstring"""






							snake_case_						       =	[self._fetch_inputs(__lowercase		) for ids in input_ids]
							return zip(*__lowercase		)






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	list		):






							"""simple docstring"""






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







def 		lowerCamelCase__       (						_A	,      _A	,      _A=None				):







	'''simple docstring'''





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


@partial(jax.pmap	,      axis_name="batch"				)
def 		lowerCamelCase__       (						_A	,      _A	,      **_A				):







	'''simple docstring'''





	def loss_fn(_A				):
		snake_case_						       =	model_inputs.pop("start_labels"				)
		snake_case_						       =	model_inputs.pop("end_labels"				)
		snake_case_						       =	model_inputs.pop("pooled_labels"				)

		snake_case_						       =	state.apply_fn(**_A	,      params=_A	,      dropout_rng=_A	,      train=_A				)
		snake_case_       , snake_case_       , snake_case_						       =	outputs

		return state.loss_fn(
		    _A	,      _A	,      _A	,      _A	,      _A	,      _A	,      )

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

	snake_case_						       =	state.apply_gradients(grads=_A				)
	return state, metrics, new_drp_rng


@partial(jax.pmap	,      axis_name="batch"				)
def 		lowerCamelCase__       (						_A	,      **_A				):







	'''simple docstring'''





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

	snake_case_						       =	state.apply_fn(**_A	,      params=state.params	,      train=_A				)
	snake_case_       , snake_case_       , snake_case_						       =	outputs

	snake_case_						       =	state.loss_fn(_A	,      _A	,      _A	,      _A	,      _A	,      _A				)
	snake_case_						       =	jax.lax.pmean({"loss": loss}	,      axis_name="batch"				)
	return metrics
class 						UpperCAmelCase  (							train_state.TrainState     ):







						'''simple docstring'''





						lowerCAmelCase_							=  struct.field(pytree_node=UpperCAmelCase__     )







@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  None






						def 			snake_case__		(		self				:	int			,      __lowercase				:	Any			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Optional[int]			,      __lowercase				:	Optional[int]=None		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Optional[Any]			,      __lowercase				:	str			,      __lowercase				:	Tuple			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	self.args
							snake_case_						       =	len(__lowercase		) // args.batch_size

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

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

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






						def 			snake_case__		(		self				:	str			,      __lowercase				:	List[Any]			,      __lowercase				:	str		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	int			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






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







def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





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

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

	snake_case_						       =	joblib.load(os.path.join(_A	,      "args.joblib"				)				)
	snake_case_						       =	joblib.load(os.path.join(_A	,      "data_collator.joblib"				)				)

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

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


def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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






def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





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

	snake_case_						       =	scheduler_fn(_A	,      _A	,      _A	,      _A				)

	snake_case_						       =	optax.adamw(learning_rate=_A	,      weight_decay=_A	,      mask=_A				)
	return tx, lr






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


lowercase__							:	Union[str, Any]											=		{"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Tuple											=		["DPTFeatureExtractor"]
		lowercase__							:	Optional[Any]											=		["DPTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase__							:	Dict											=		[
		    "DPT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DPTForDepthEstimation",
		    "DPTForSemanticSegmentation",
		    "DPTModel",
		    "DPTPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_dpt import DPTFeatureExtractor
				from .image_processing_dpt import DPTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dpt import (
				    DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DPTForDepthEstimation,
				    DPTForSemanticSegmentation,
				    DPTModel,
				    DPTPreTrainedModel,
				)


else:
		import sys

		lowercase__							:	List[Any]											=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
