


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      ):
		_a     :  BigBirdConfig
		_a     :  jnp.dtype           =     jnp.floataa
		_a     :  bool           =     True
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





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






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




									"""simple docstring"""





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



class 		__a				(    UpperCAmelCase      ):
		_a     :  List[str]           =     FlaxBigBirdForNaturalQuestionsModule



def 	lowerCAmelCase__			(      a__:		List[str]      ,  a__:		Dict      ,  a__:		Dict      ,  a__:		Any      ,  a__:		Dict      ,  a__:		Union[str, Any]	)  ->		Any:


							'''simple docstring'''





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

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

@dataclass
class 		__a				:
		_a     :  str           =     "google/bigbird-roberta-base"
		_a     :  int           =     30_00
		_a     :  int           =     1_05_00

		_a     :  int           =     1_28
		_a     :  int           =     3

		_a     :  int           =     1
		_a     :  int           =     5

		# tx_args
		_a     :  float           =     3E-5
		_a     :  float           =     0.0
		_a     :  int           =     2_00_00
		_a     :  float           =     0.0_0_9_5

		_a     :  str           =     "bigbird-roberta-natural-questions"
		_a     :  str           =     "training-expt"
		_a     :  str           =     "data/nq-training.jsonl"
		_a     :  str           =     "data/nq-validation.jsonl"
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





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



@dataclass
class 		__a				:
		_a     :  int
		_a     :  int           =     40_96  # no dynamic padding on TPUs
		def __call__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase					=      self.collate_fn(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      jax.tree_util.tree_map(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
									return batch
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.fetch_inputs(features['input_ids']						)
									_UpperCAmelCase					=      {
									    '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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





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






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




									"""simple docstring"""





									_UpperCAmelCase					=      [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 	lowerCAmelCase__			(      a__:		Union[str, Any]      ,  a__:		Dict      ,  a__:		List[str]=None	)  ->		int:


							'''simple docstring'''





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

@partial(jax.pmap      ,  axis_name='batch'	)
def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Tuple      ,  **a__:		Dict	)  ->		int:


							'''simple docstring'''





							def loss_fn(a__:		Union[str, Any]	):
														_UpperCAmelCase					=      model_inputs.pop('start_labels'	)
														_UpperCAmelCase					=      model_inputs.pop('end_labels'	)
														_UpperCAmelCase					=      model_inputs.pop('pooled_labels'	)

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

														return state.loss_fn(
														    a__      ,  a__      ,  a__      ,  a__      ,  a__      ,  a__      ,  )

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

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

@partial(jax.pmap      ,  axis_name='batch'	)
def 	lowerCAmelCase__			(      a__:		List[Any]      ,  **a__:		Tuple	)  ->		int:


							'''simple docstring'''





							_UpperCAmelCase					=      model_inputs.pop('start_labels'	)
							_UpperCAmelCase					=      model_inputs.pop('end_labels'	)
							_UpperCAmelCase					=      model_inputs.pop('pooled_labels'	)

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

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

class 		__a				(    train_state.TrainState      ):
		_a     :  Callable           =     struct.field(pytree_node=UpperCAmelCase      )



@dataclass
class 		__a				:
		_a     :  Args
		_a     :  Callable
		_a     :  Callable
		_a     :  Callable
		_a     :  Callable
		_a     :  wandb
		_a     :  Callable           =     None
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=None						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      model.params
									_UpperCAmelCase					=      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:
																_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      restore_checkpoint(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      {
																    'lr': args.lr,
																    'init_lr': args.init_lr,
																    'warmup_steps': args.warmup_steps,
																    'num_train_steps': num_train_steps,
																    'weight_decay': args.weight_decay,
																}
																_UpperCAmelCase		,	_UpperCAmelCase					=      build_tx(**_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      train_state.TrainState(
																    step=_SCREAMING_SNAKE_CASE      ,   apply_fn=model.__call__      ,   params=_SCREAMING_SNAKE_CASE      ,   tx=_SCREAMING_SNAKE_CASE      ,   opt_state=_SCREAMING_SNAKE_CASE      ,   )
																_UpperCAmelCase					=      args
																_UpperCAmelCase					=      data_collator
																_UpperCAmelCase					=      lr
																_UpperCAmelCase					=      params
									_UpperCAmelCase					=      jax_utils.replicate(_SCREAMING_SNAKE_CASE						)
									return state
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.args
									_UpperCAmelCase					=      len(_SCREAMING_SNAKE_CASE						) // args.batch_size

									_UpperCAmelCase					=      jax.random.PRNGKey(0						)
									_UpperCAmelCase					=      jax.random.split(_SCREAMING_SNAKE_CASE      ,   jax.device_count()						)
									for epoch in range(args.max_epochs						):
																_UpperCAmelCase					=      jnp.array(0      ,   dtype=jnp.floataa						)
																_UpperCAmelCase					=      get_batched_dataset(_SCREAMING_SNAKE_CASE      ,   args.batch_size      ,   seed=_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase					=      0
																for batch in tqdm(_SCREAMING_SNAKE_CASE      ,   total=_SCREAMING_SNAKE_CASE      ,   desc=f'''Running EPOCH-{epoch}'''						):
																							_UpperCAmelCase					=      self.data_collator(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      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:
																														_UpperCAmelCase					=      jax_utils.unreplicate(state.step						)
																														_UpperCAmelCase					=      running_loss.item() / i
																														_UpperCAmelCase					=      self.scheduler_fn(state_step - 1						)

																														_UpperCAmelCase					=      self.evaluate(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
																														_UpperCAmelCase					=      {
																														    '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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Union[str, Any]:




									"""simple docstring"""





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






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




									"""simple docstring"""





									_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		Union[str, Any]      ,  a__:		List[Any]	)  ->		List[str]:


							'''simple docstring'''





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

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

							_UpperCAmelCase					=      joblib.load(os.path.join(a__      ,  'args.joblib'	)	)
							_UpperCAmelCase					=      joblib.load(os.path.join(a__      ,  'data_collator.joblib'	)	)

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

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

def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		Optional[int]      ,  a__:		str      ,  a__:		int	)  ->		Optional[int]:


							'''simple docstring'''





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







def 	lowerCAmelCase__			(      a__:		int      ,  a__:		Optional[Any]      ,  a__:		Union[str, Any]      ,  a__:		int      ,  a__:		Tuple	)  ->		str:


							'''simple docstring'''





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

							_UpperCAmelCase					=      scheduler_fn(a__      ,  a__      ,  a__      ,  a__	)

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




from __future__ import annotations

from PIL import Image

# Define glider example
lowerCAmelCase__  :str             =							[
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
lowerCAmelCase__  :Any             =							[[0, 1, 0], [0, 1, 0], [0, 1, 0]]

def 	lowerCAmelCase__			(      a__:		list[list[int]]	)  ->		list[list[int]]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for i in range(len(a__	)	):
														_UpperCAmelCase					=      []
														for j in range(len(cells[i]	)	):
																					# Get the number of live neighbours
																					_UpperCAmelCase					=      0
																					if i > 0 and j > 0:
																												neighbour_count += cells[i - 1][j - 1]
																					if i > 0:
																												neighbour_count += cells[i - 1][j]
																					if i > 0 and j < len(cells[i]	) - 1:
																												neighbour_count += cells[i - 1][j + 1]
																					if j > 0:
																												neighbour_count += cells[i][j - 1]
																					if j < len(cells[i]	) - 1:
																												neighbour_count += cells[i][j + 1]
																					if i < len(a__	) - 1 and j > 0:
																												neighbour_count += cells[i + 1][j - 1]
																					if i < len(a__	) - 1:
																												neighbour_count += cells[i + 1][j]
																					if i < len(a__	) - 1 and j < len(cells[i]	) - 1:
																												neighbour_count += cells[i + 1][j + 1]

																					# Rules of the game of life (excerpt from Wikipedia):
																					# 1. Any live cell with two or three live neighbours survives.
																					# 2. Any dead cell with three live neighbours becomes a live cell.
																					# 3. All other live cells die in the next generation.
																					#    Similarly, all other dead cells stay dead.
																					_UpperCAmelCase					=      cells[i][j] == 1
																					if (
																					    (alive and 2 <= neighbour_count <= 3)
																					    or not alive
																					    and neighbour_count == 3
																					):
																												next_generation_row.append(1	)
																					else:
																												next_generation_row.append(0	)

														next_generation.append(a__	)
							return next_generation

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		int	)  ->		list[Image.Image]:


							'''simple docstring'''





							_UpperCAmelCase					=      []
							for _ in range(a__	):
														# Create output image
														_UpperCAmelCase					=      Image.new('RGB'      ,  (len(cells[0]	), len(a__	))	)
														_UpperCAmelCase					=      img.load()

														# Save cells to image
														for x in range(len(a__	)	):
																					for y in range(len(cells[0]	)	):
																												_UpperCAmelCase					=      2_5_5 - cells[y][x] * 2_5_5
																												_UpperCAmelCase					=      (colour, colour, colour)

        # Save image
														images.append(a__	)
														_UpperCAmelCase					=      new_generation(a__	)
							return images


if __name__ == "__main__":
		lowerCAmelCase__  :Tuple             =							generate_images(GLIDER, 1_6)
		images[0].save('''out.gif''', save_all=True, append_images=images[1:])
