



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      (   UpperCamelCase_):







							'''simple docstring'''






							UpperCamelCase__				:      BigBirdConfig
							UpperCamelCase__				:      jnp.dtype         =					jnp.floataa
							UpperCamelCase__				:      bool         =					True


							def _a      (							self		):
											super().setup()
											a__				      =		nn.Dense(5  ,  dtype=self.dtype		)



							def __call__(							self  ,  *a_  ,  **a_		):
											a__				      =		super().__call__(*_a  ,  **_a		)
											a__				      =		self.cls(outputs[2]		)
											return outputs[:2] + (cls_out,)




class  __snake_case      (   UpperCamelCase_):







							'''simple docstring'''






							UpperCamelCase__				:      List[str]         =					FlaxBigBirdForNaturalQuestionsModule




def 			A_      (	__a     :				Optional[int] ,		__a     :				int ,		__a     :				Dict ,		__a     :				int ,		__a     :				Optional[int] ,		__a     :				Union[str, Any]		):

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

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

@dataclass
class  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      str         =					"google/bigbird-roberta-base"
							UpperCamelCase__				:      int         =					3_0_0_0
							UpperCamelCase__				:      int         =					1_0_5_0_0

							UpperCamelCase__				:      int         =					1_2_8
							UpperCamelCase__				:      int         =					3

							UpperCamelCase__				:      int         =					1
							UpperCamelCase__				:      int         =					5

							# tx_args
							UpperCamelCase__				:      float         =					3e-5
							UpperCamelCase__				:      float         =					0.0
							UpperCamelCase__				:      int         =					2_0_0_0_0
							UpperCamelCase__				:      float         =					0.0095

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


							def _a      (							self		):
											os.makedirs(self.base_dir  ,  exist_ok=_a		)
											a__				      =		os.path.join(self.base_dir  ,  self.save_dir		)
											a__				      =		self.batch_size_per_device * jax.device_count()




@dataclass
class  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      int
							UpperCamelCase__				:      int         =					4_0_9_6  # no dynamic padding on TPUs


							def __call__(							self  ,  a_		):
											a__				      =		self.collate_fn(_a		)
											a__				      =		jax.tree_util.tree_map(_a  ,  _a		)
											return batch


							def _a      (							self  ,  a_		):
											a__				      =		self.fetch_inputs(features["""input_ids"""]		)
											a__				      =		{
											    """input_ids""": jnp.array(_a  ,  dtype=jnp.intaa		),
											    """attention_mask""": jnp.array(_a  ,  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 _a      (							self  ,  a_		):
											a__				      =		[self._fetch_inputs(_a		) for ids in input_ids]
											return zip(*_a		)



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




def 			A_      (	__a     :				Optional[Any] ,		__a     :				str ,		__a     :				List[Any]=None		):

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

@partial(jax.pmap ,		axis_name="""batch"""		)
def 			A_      (	__a     :				List[str] ,		__a     :				Union[str, Any] ,		**__a     :				Union[str, Any]		):

				"""simple docstring"""
				def loss_fn(__a     :				Union[str, Any]		):
								a__				      =		model_inputs.pop("""start_labels"""		)
								a__				      =		model_inputs.pop("""end_labels"""		)
								a__				      =		model_inputs.pop("""pooled_labels"""		)

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

								return state.loss_fn(
								    __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		)

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

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

@partial(jax.pmap ,		axis_name="""batch"""		)
def 			A_      (	__a     :				int ,		**__a     :				Tuple		):

				"""simple docstring"""
				a__				      =		model_inputs.pop("""start_labels"""		)
				a__				      =		model_inputs.pop("""end_labels"""		)
				a__				      =		model_inputs.pop("""pooled_labels"""		)

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

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

class  __snake_case      (   train_state.TrainState):







							'''simple docstring'''






							UpperCamelCase__				:      Callable         =					struct.field(pytree_node=UpperCamelCase_)




@dataclass
class  __snake_case      :







							'''simple docstring'''






							UpperCamelCase__				:      Args
							UpperCamelCase__				:      Callable
							UpperCamelCase__				:      Callable
							UpperCamelCase__				:      Callable
							UpperCamelCase__				:      Callable
							UpperCamelCase__				:      wandb
							UpperCamelCase__				:      Callable         =					None


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_=None		):
											a__				      =		model.params
											a__				      =		TrainState.create(
											    apply_fn=model.__call__  ,  params=_a  ,  tx=_a  ,  loss_fn=_a  ,  )
											if ckpt_dir is not None:
															a__				      =		restore_checkpoint(_a  ,  _a		)
															a__				      =		{
															    """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__				      =		build_tx(**_a		)
															a__				      =		train_state.TrainState(
															    step=_a  ,  apply_fn=model.__call__  ,  params=_a  ,  tx=_a  ,  opt_state=_a  ,  )
															a__				      =		args
															a__				      =		data_collator
															a__				      =		lr
															a__				      =		params
											a__				      =		jax_utils.replicate(_a		)
											return state


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		self.args
											a__				      =		len(_a		) // args.batch_size

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

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

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


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



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




def 			A_      (	__a     :				str ,		__a     :				Any		):

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

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

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

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

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

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				Optional[Any] ,		__a     :				int ,		__a     :				List[str]		):

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




def 			A_      (	__a     :				int ,		__a     :				str ,		__a     :				Any ,		__a     :				Dict ,		__a     :				Dict		):

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

				a__				      =		scheduler_fn(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase		)

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






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase							 =					299_792_458

# Symbols
UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					symbols("""ct x y z""")

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				if velocity > c:
								raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""		)
				elif velocity < 1:
								# Usually the speed should be much higher than 1 (c order of magnitude)
								raise ValueError("""Speed must be greater than or equal to 1!"""		)

				return velocity / c

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return 1 / sqrt(1 - beta(__a		) ** 2		)

def 			A_      (	__a     :				float		):

				"""simple docstring"""
				return np.array(
				    [
				        [gamma(__a		), -gamma(__a		) * beta(__a		), 0, 0],
				        [-gamma(__a		) * beta(__a		), gamma(__a		), 0, 0],
				        [0, 0, 1, 0],
				        [0, 0, 0, 1],
				    ]		)

def 			A_      (	__a     :				float ,		__a     :				np.ndarray | None = None		):

				"""simple docstring"""
				# Ensure event is not empty
				if event is None:
								a__				      =		np.array([ct, x, y, z]		)  # Symbolic four vector
				else:
								event[0] *= c  # x0 is ct (speed of light * time)

				return transformation_matrix(__a		) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Example of symbolic vector:
	UpperCAmelCase							 =					transform(29_979_245)
	print("""Example of four vector: """)
	print(f"""ct' = {four_vector[0]}""")
	print(f"""x' = {four_vector[1]}""")
	print(f"""y' = {four_vector[2]}""")
	print(f"""z' = {four_vector[3]}""")

	# Substitute symbols with numerical values
	UpperCAmelCase							 =					{ct: c, x: 1, y: 1, z: 1}
	UpperCAmelCase							 =					[four_vector[i].subs(sub_dict) for i in range(4)]

	print(f"""\n{numerical_vector}""")

