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						( lowerCamelCase__  ):


				'''simple docstring'''






				lowerCamelCase_    = 42
				lowerCamelCase_    = jnp.floataa
				lowerCamelCase_    = True
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						super().setup()
						A_		:    int         =				nn.Dense(5		,     dtype=self.dtype )







				def __call__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						A_		:    Union[str, Any]         =				super().__call__(*lowercase		,     **lowercase )
						A_		:    List[Any]         =				self.cls(outputs[2] )
						return outputs[:2] + (cls_out,)





class UpperCAmelCase						( lowerCamelCase__  ):


				'''simple docstring'''






				lowerCamelCase_    = FlaxBigBirdForNaturalQuestionsModule




def     UpperCamelCase						(	__lowercase						:			Dict						,__lowercase						:			List[Any]						,__lowercase						:			Optional[int]						,__lowercase						:			Dict						,__lowercase						:			Tuple						,__lowercase						:			List[Any]     ):
		'''simple docstring'''




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

		A_		:    Dict         =				partial(__snake_case						,reduction=jnp.mean     )
		A_		:    Dict         =				cross_entropy(__snake_case						,__snake_case     )
		A_		:    Union[str, Any]         =				cross_entropy(__snake_case						,__snake_case     )
		A_		:    Optional[int]         =				cross_entropy(__snake_case						,__snake_case     )
		return (start_loss + end_loss + pooled_loss) / 3





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = "google/bigbird-roberta-base"
				lowerCamelCase_    = 3_0_0_0
				lowerCamelCase_    = 1_0_5_0_0

				lowerCamelCase_    = 1_2_8
				lowerCamelCase_    = 3

				lowerCamelCase_    = 1
				lowerCamelCase_    = 5

				# tx_args
				lowerCamelCase_    = 3E-5
				lowerCamelCase_    = 0.0
				lowerCamelCase_    = 2_0_0_0_0
				lowerCamelCase_    = 0.0_095

				lowerCamelCase_    = "bigbird-roberta-natural-questions"
				lowerCamelCase_    = "training-expt"
				lowerCamelCase_    = "data/nq-training.jsonl"
				lowerCamelCase_    = "data/nq-validation.jsonl"
				def   lowerCAmelCase_       (						self ):



						"""simple docstring"""





						os.makedirs(self.base_dir		,     exist_ok=lowercase )
						A_		:    int         =				os.path.join(self.base_dir		,     self.save_dir )
						A_		:    Tuple         =				self.batch_size_per_device * jax.device_count()





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = 42
				lowerCamelCase_    = 4_0_9_6  # no dynamic padding on TPUs
				def __call__(						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Tuple         =				self.collate_fn(lowercase )
						A_		:    int         =				jax.tree_util.tree_map(lowercase		,     lowercase )
						return batch
				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_       ,      A_		:    int         =				self.fetch_inputs(features['input_ids'] )
						A_		:    Optional[Any]         =				{
						    '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   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Optional[int]         =				[self._fetch_inputs(lowercase ) for ids in input_ids]
						return zip(*lowercase )







				def   lowerCAmelCase_       (						self		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				[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     UpperCamelCase						(	__lowercase						:			List[Any]						,__lowercase						:			int						,__lowercase						:			Any=None     ):
		'''simple docstring'''




		if seed is not None:
				A_		:    Optional[int]         =				dataset.shuffle(seed=__snake_case     )
		for i in range(len(__snake_case     ) // batch_size     ):
				A_		:    List[str]         =				dataset[i * batch_size : (i + 1) * batch_size]
				yield dict(__snake_case     )




@partial(jax.pmap						,axis_name='batch'     )
def     UpperCamelCase						(	__lowercase						:			Any						,__lowercase						:			Dict						,**__lowercase						:			Dict     ):
		'''simple docstring'''




		def loss_fn(__lowercase						:			Any     ):
				A_		:    Optional[int]         =				model_inputs.pop('start_labels'     )
				A_		:    Union[str, Any]         =				model_inputs.pop('end_labels'     )
				A_		:    Optional[Any]         =				model_inputs.pop('pooled_labels'     )

				A_		:    Dict         =				state.apply_fn(**__snake_case						,params=__snake_case						,dropout_rng=__snake_case						,train=__snake_case     )
				A_       ,      A_       ,      A_		:    List[Any]         =				outputs

				return state.loss_fn(
				    __snake_case						,__snake_case						,__snake_case						,__snake_case						,__snake_case						,__snake_case						,)

		A_       ,      A_		:    Dict         =				jax.random.split(__snake_case     )
		A_		:    int         =				jax.value_and_grad(__snake_case     )
		A_       ,      A_		:    str         =				grad_fn(state.params     )
		A_		:    Dict         =				jax.lax.pmean({'loss': loss}						,axis_name='batch'     )
		A_		:    List[Any]         =				jax.lax.pmean(__snake_case						,'batch'     )

		A_		:    int         =				state.apply_gradients(grads=__snake_case     )
		return state, metrics, new_drp_rng




@partial(jax.pmap						,axis_name='batch'     )
def     UpperCamelCase						(	__lowercase						:			Union[str, Any]						,**__lowercase						:			Union[str, Any]     ):
		'''simple docstring'''




		A_		:    Union[str, Any]         =				model_inputs.pop('start_labels'     )
		A_		:    int         =				model_inputs.pop('end_labels'     )
		A_		:    List[Any]         =				model_inputs.pop('pooled_labels'     )

		A_		:    str         =				state.apply_fn(**__snake_case						,params=state.params						,train=__snake_case     )
		A_       ,      A_       ,      A_		:    Optional[Any]         =				outputs

		A_		:    Optional[int]         =				state.loss_fn(__snake_case						,__snake_case						,__snake_case						,__snake_case						,__snake_case						,__snake_case     )
		A_		:    List[str]         =				jax.lax.pmean({'loss': loss}						,axis_name='batch'     )
		return metrics





class UpperCAmelCase						( train_state.TrainState  ):


				'''simple docstring'''






				lowerCamelCase_    = struct.field(pytree_node=lowerCamelCase__  )





@dataclass
class UpperCAmelCase						:


				'''simple docstring'''






				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				lowerCamelCase_    = None
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase		,     lowercase=None ):



						"""simple docstring"""





						A_		:    Dict         =				model.params
						A_		:    Tuple         =				TrainState.create(
						    apply_fn=model.__call__		,     params=lowercase		,     tx=lowercase		,     loss_fn=lowercase		,     )
						if ckpt_dir is not None:
								A_       ,      A_       ,      A_       ,      A_       ,      A_		:    str         =				restore_checkpoint(lowercase		,     lowercase )
								A_		:    List[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,
								}
								A_       ,      A_		:    List[Any]         =				build_tx(**lowercase )
								A_		:    str         =				train_state.TrainState(
								    step=lowercase		,     apply_fn=model.__call__		,     params=lowercase		,     tx=lowercase		,     opt_state=lowercase		,     )
								A_		:    Optional[Any]         =				args
								A_		:    str         =				data_collator
								A_		:    List[str]         =				lr
								A_		:    List[str]         =				params
						A_		:    str         =				jax_utils.replicate(lowercase )
						return state
				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    Any         =				self.args
						A_		:    str         =				len(lowercase ) // args.batch_size

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

												A_		:    List[Any]         =				self.evaluate(lowercase		,     lowercase )
												A_		:    str         =				{
												    '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   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





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







				def   lowerCAmelCase_       (						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						A_		:    int         =				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     UpperCamelCase						(	__lowercase						:			Tuple						,__lowercase						:			List[str]     ):
		'''simple docstring'''




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

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

		A_		:    int         =				joblib.load(os.path.join(__snake_case						,'args.joblib'     )     )
		A_		:    Optional[Any]         =				joblib.load(os.path.join(__snake_case						,'data_collator.joblib'     )     )

		with open(os.path.join(__snake_case						,'training_state.json'     )						,'r'     ) as f:
				A_		:    Union[str, Any]         =				json.load(__snake_case     )
		A_		:    Union[str, Any]         =				training_state['step']

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




def     UpperCamelCase						(	__lowercase						:			List[str]						,__lowercase						:			Dict						,__lowercase						:			Optional[int]						,__lowercase						:			str     ):
		'''simple docstring'''




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




def     UpperCamelCase						(	__lowercase						:			Optional[int]						,__lowercase						:			Optional[int]						,__lowercase						:			List[str]						,__lowercase						:			Tuple						,__lowercase						:			str     ):
		'''simple docstring'''




		def weight_decay_mask(__lowercase						:			Optional[int]     ):
				A_		:    Union[str, Any]         =				traverse_util.flatten_dict(__snake_case     )
				A_		:    Dict         =				{k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
				return traverse_util.unflatten_dict(__snake_case     )

		A_		:    Optional[Any]         =				scheduler_fn(__snake_case						,__snake_case						,__snake_case						,__snake_case     )

		A_		:    Union[str, Any]         =				optax.adamw(learning_rate=__snake_case						,weight_decay=__snake_case						,mask=__snake_case     )
		return tx, lr


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
	from .pipeline_unclip import UnCLIPPipeline
	from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
	from .text_proj import UnCLIPTextProjModel






