


'''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 _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







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







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			Union[str, Any]:
			super().setup()
			__magic_name__	:   List[Any]					     =					nn.Dense(5  ,					dtype=self.dtype      )







		def __call__( self		:						int  ,					*_A		:						str  ,					**_A		:						List[str]      )  ->			Optional[Any]:
			__magic_name__	:   List[str]					     =					super().__call__(*_A  ,					**_A      )
			__magic_name__	:   int					     =					self.cls(outputs[2]      )
			return outputs[:2] + (cls_out,)







class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      List[str]				   =    FlaxBigBirdForNaturalQuestionsModule







def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     Union[str, Any]		):





	"""simple docstring"""







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

	__magic_name__	:   Tuple					     =					partial(lowerCAmelCase		,		reduction=jnp.mean		)
	__magic_name__	:   Union[str, Any]					     =					cross_entropy(lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   int					     =					cross_entropy(lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   Optional[int]					     =					cross_entropy(lowerCAmelCase		,		lowerCAmelCase		)
	return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







		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				   =    0.0095

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







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			List[Any]:
			os.makedirs(self.base_dir  ,					exist_ok=_A      )
			__magic_name__	:   int					     =					os.path.join(self.base_dir  ,					self.save_dir      )
			__magic_name__	:   Optional[int]					     =					self.batch_size_per_device * jax.device_count()







@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







		A_     :      int
		A_     :      int				   =    40_96  # no dynamic padding on TPUs







		def __call__( self		:						Any  ,					_A		:						Any      )  ->			List[Any]:
			__magic_name__	:   List[str]					     =					self.collate_fn(_A      )
			__magic_name__	:   str					     =					jax.tree_util.tree_map(_A  ,					_A      )
			return batch







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						Dict      )  ->			Tuple:
			__magic_name__	:   List[str]					     =					self.fetch_inputs(features['input_ids']      )
			__magic_name__	:   List[Any]					     =					{
			    '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        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						list      )  ->			Optional[int]:
			__magic_name__	:   Any					     =					[self._fetch_inputs(_A      ) for ids in input_ids]
			return zip(*_A      )







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						list      )  ->			Any:
			__magic_name__	:   List[Any]					     =					[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 lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     List[str]=None		):





	"""simple docstring"""







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





@partial(jax.pmap		,		axis_name='batch'		)
def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Union[str, Any]		,		**lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	def loss_fn(lowerCAmelCase    :     List[Any]		):
		__magic_name__	:   int					     =					model_inputs.pop('start_labels'		)
		__magic_name__	:   Any					     =					model_inputs.pop('end_labels'		)
		__magic_name__	:   Any					     =					model_inputs.pop('pooled_labels'		)

		__magic_name__	:   Optional[int]					     =					state.apply_fn(**lowerCAmelCase		,		params=lowerCAmelCase		,		dropout_rng=lowerCAmelCase		,		train=lowerCAmelCase		)
		__magic_name__	:   Dict					     =					outputs

		return state.loss_fn(
		    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		)

	__magic_name__	:   List[Any]					     =					jax.random.split(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					jax.value_and_grad(lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					grad_fn(state.params		)
	__magic_name__	:   Union[str, Any]					     =					jax.lax.pmean({'loss': loss}		,		axis_name='batch'		)
	__magic_name__	:   List[str]					     =					jax.lax.pmean(lowerCAmelCase		,		'batch'		)

	__magic_name__	:   Union[str, Any]					     =					state.apply_gradients(grads=lowerCAmelCase		)
	return state, metrics, new_drp_rng





@partial(jax.pmap		,		axis_name='batch'		)
def lowerCamelCase      (      lowerCAmelCase    :     Any		,		**lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	__magic_name__	:   Any					     =					model_inputs.pop('start_labels'		)
	__magic_name__	:   Tuple					     =					model_inputs.pop('end_labels'		)
	__magic_name__	:   Any					     =					model_inputs.pop('pooled_labels'		)

	__magic_name__	:   Any					     =					state.apply_fn(**lowerCAmelCase		,		params=state.params		,		train=lowerCAmelCase		)
	__magic_name__	:   str					     =					outputs

	__magic_name__	:   str					     =					state.loss_fn(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)
	__magic_name__	:   Any					     =					jax.lax.pmean({'loss': loss}		,		axis_name='batch'		)
	return metrics
class _lowerCamelCase	(						train_state.TrainState							):



		'''simple docstring'''







		A_     :      Callable				   =    struct.field(pytree_node=lowercase__							)







@dataclass
class _lowerCamelCase	:



		'''simple docstring'''







		A_     :      Args
		A_     :      Callable
		A_     :      Callable
		A_     :      Callable
		A_     :      Callable
		A_     :      wandb
		A_     :      Callable				   =    None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						List[Any]  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						List[Any]=None      )  ->			int:
			__magic_name__	:   Dict					     =					model.params
			__magic_name__	:   List[Any]					     =					TrainState.create(
			    apply_fn=model.__call__  ,					params=_A  ,					tx=_A  ,					loss_fn=_A  ,					)
			if ckpt_dir is not None:
				__magic_name__	:   Tuple					     =					restore_checkpoint(_A  ,					_A      )
				__magic_name__	:   Optional[Any]					     =					{
				    'lr': args.lr,
				    'init_lr': args.init_lr,
				    'warmup_steps': args.warmup_steps,
				    'num_train_steps': num_train_steps,
				    'weight_decay': args.weight_decay,
				}
				__magic_name__	:   int					     =					build_tx(**_A      )
				__magic_name__	:   Optional[Any]					     =					train_state.TrainState(
				    step=_A  ,					apply_fn=model.__call__  ,					params=_A  ,					tx=_A  ,					opt_state=_A  ,					)
				__magic_name__	:   Optional[Any]					     =					args
				__magic_name__	:   str					     =					data_collator
				__magic_name__	:   Any					     =					lr
				__magic_name__	:   List[str]					     =					params
			__magic_name__	:   str					     =					jax_utils.replicate(_A      )
			return state







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Dict  ,					_A		:						Dict  ,					_A		:						str      )  ->			Tuple:
			__magic_name__	:   Any					     =					self.args
			__magic_name__	:   Dict					     =					len(_A      ) // args.batch_size

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

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







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						List[str]  ,					_A		:						List[Any]      )  ->			int:
			__magic_name__	:   Dict					     =					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 lowerCamelCase      (      lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	print(f'RESTORING CHECKPOINT FROM {save_dir}'		,		end=' ... '		)
	with open(os.path.join(lowerCAmelCase		,		'flax_model.msgpack'		)		,		'rb'		) as f:
		__magic_name__	:   Union[str, Any]					     =					from_bytes(state.params		,		f.read()		)

	with open(os.path.join(lowerCAmelCase		,		'opt_state.msgpack'		)		,		'rb'		) as f:
		__magic_name__	:   List[str]					     =					from_bytes(state.opt_state		,		f.read()		)

	__magic_name__	:   Union[str, Any]					     =					joblib.load(os.path.join(lowerCAmelCase		,		'args.joblib'		)		)
	__magic_name__	:   Optional[Any]					     =					joblib.load(os.path.join(lowerCAmelCase		,		'data_collator.joblib'		)		)

	with open(os.path.join(lowerCAmelCase		,		'training_state.json'		)		,		'r'		) as f:
		__magic_name__	:   Any					     =					json.load(lowerCAmelCase		)
	__magic_name__	:   Tuple					     =					training_state['step']

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





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     int		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					num_train_steps - warmup_steps
	__magic_name__	:   str					     =					optax.linear_schedule(init_value=lowerCAmelCase		,		end_value=lowerCAmelCase		,		transition_steps=lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					optax.linear_schedule(init_value=lowerCAmelCase		,		end_value=1e-7		,		transition_steps=lowerCAmelCase		)
	__magic_name__	:   Any					     =					optax.join_schedules(schedules=[warmup_fn, decay_fn]		,		boundaries=[warmup_steps]		)
	return lr





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







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

	__magic_name__	:   List[Any]					     =					scheduler_fn(lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Optional[Any]					     =					optax.adamw(learning_rate=lowerCAmelCase		,		weight_decay=lowerCAmelCase		,		mask=lowerCAmelCase		)
	return tx, lr



'''simple docstring'''

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase				:Optional[Any]        =      transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)





def lowerCamelCase      (      lowerCAmelCase    :     Tuple		):





	"""simple docstring"""







	if isinstance(lowerCAmelCase		,		torch.Tensor		):
		return image
	elif isinstance(lowerCAmelCase		,		PIL.Image.Image		):
		__magic_name__	:   List[Any]					     =					[image]

	__magic_name__	:   List[Any]					     =					[trans(img.convert('RGB'		)		) for img in image]
	__magic_name__	:   Dict					     =					torch.stack(lowerCAmelCase		)
	return image





class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		def __init__( self		:						Optional[Any]  ,					_A		:						str  ,					_A		:						int      )  ->			Dict:
			super().__init__()

			# make sure scheduler can always be converted to DDIM
			__magic_name__	:   Optional[int]					     =					DDIMScheduler.from_config(scheduler.config      )

			self.register_modules(unet=_A  ,					scheduler=_A      )







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						Union[str, Any]      )  ->			Optional[int]:
			if strength < 0 or strength > 1:
				raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'      )







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						List[str]  ,					_A		:						Optional[Any]  ,					_A		:						int      )  ->			List[Any]:
			# get the original timestep using init_timestep
			__magic_name__	:   Tuple					     =					min(int(num_inference_steps * strength      )  ,					_A      )

			__magic_name__	:   Any					     =					max(num_inference_steps - init_timestep  ,					0      )
			__magic_name__	:   List[str]					     =					self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start







		def        __lowerCAmelCase		( self		:						Any  ,					_A		:						str  ,					_A		:						Optional[int]  ,					_A		:						Tuple  ,					_A		:						List[str]  ,					_A		:						str  ,					_A		:						Optional[int]=None      )  ->			Dict:
			if not isinstance(_A  ,					(torch.Tensor, PIL.Image.Image, list)      ):
				raise ValueError(
				    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A      )}'      )

			__magic_name__	:   Union[str, Any]					     =					image.to(device=_A  ,					dtype=_A      )

			if isinstance(_A  ,					_A      ) and len(_A      ) != batch_size:
				raise ValueError(
				    F'You have passed a list of generators of length {len(_A      )}, but requested an effective batch'
				    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

			__magic_name__	:   Tuple					     =					init_latents.shape
			__magic_name__	:   Any					     =					randn_tensor(_A  ,					generator=_A  ,					device=_A  ,					dtype=_A      )

			# get latents
			print('add noise to latents at timestep'  ,					_A      )
			__magic_name__	:   List[str]					     =					self.scheduler.add_noise(_A  ,					_A  ,					_A      )
			__magic_name__	:   List[str]					     =					init_latents

			return latents







		@torch.no_grad()
		def __call__( self		:						Tuple  ,					_A		:						Union[torch.FloatTensor, PIL.Image.Image] = None  ,					_A		:						float = 0.8  ,					_A		:						int = 1  ,					_A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,					_A		:						float = 0.0  ,					_A		:						int = 50  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[str] = "pil"  ,					_A		:						bool = True  ,					)  ->			Union[ImagePipelineOutput, Tuple]:
			self.check_inputs(_A      )

			# 2. Preprocess image
			__magic_name__	:   int					     =					preprocess(_A      )

			# 3. set timesteps
			self.scheduler.set_timesteps(_A  ,					device=self.device      )
			__magic_name__     ,    __magic_name__	:   Dict					     =					self.get_timesteps(_A  ,					_A  ,					self.device      )
			__magic_name__	:   Dict					     =					timesteps[:1].repeat(_A      )

			# 4. Prepare latent variables
			__magic_name__	:   Optional[Any]					     =					self.prepare_latents(_A  ,					_A  ,					_A  ,					self.unet.dtype  ,					self.device  ,					_A      )
			__magic_name__	:   Optional[Any]					     =					latents

			# 5. Denoising loop
			for t in self.progress_bar(_A      ):
				# 1. predict noise model_output
				__magic_name__	:   Dict					     =					self.unet(_A  ,					_A      ).sample

				# 2. predict previous mean of image x_t-1 and add variance depending on eta
				# eta corresponds to η in paper and should be between [0, 1]
				# do x_t -> x_t-1
				__magic_name__	:   List[Any]					     =					self.scheduler.step(
				    _A  ,					_A  ,					_A  ,					eta=_A  ,					use_clipped_model_output=_A  ,					generator=_A  ,					).prev_sample

			__magic_name__	:   Union[str, Any]					     =					(image / 2 + 0.5).clamp(0  ,					1      )
			__magic_name__	:   List[Any]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      ).numpy()
			if output_type == "pil":
				__magic_name__	:   Dict					     =					self.numpy_to_pil(_A      )

			if not return_dict:
				return (image, latent_timestep.item())

			return ImagePipelineOutput(images=_A      )