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				( __lowercase ):

     '''simple docstring'''

     A_										=  42
     A_										=  jnp.floataa
     A_										=  True



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           super().setup()
           _lowerCamelCase			    =				nn.Dense(5				,				dtype=self.dtype						)





     def __call__(					self				,				*A_				,				**A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				super().__call__(*A_				,				**A_						)
           _lowerCamelCase			    =				self.cls(outputs[2]						)
           return outputs[:2] + (cls_out,)





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  FlaxBigBirdForNaturalQuestionsModule





def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Any:



      '''simple docstring'''

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

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



@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  "google/bigbird-roberta-base"
     A_										=  3_000
     A_										=  10_500

     A_										=  128
     A_										=  3

     A_										=  1
     A_										=  5

     # tx_args
     A_										=  3E-5
     A_										=  0.0
     A_										=  20_000
     A_										=  0.00_95

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



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           os.makedirs(self.base_dir				,				exist_ok=A_						)
           _lowerCamelCase			    =				os.path.join(self.base_dir				,				self.save_dir						)
           _lowerCamelCase			    =				self.batch_size_per_device * jax.device_count()





@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  4_096  # no dynamic padding on TPUs



     def __call__(					self				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.collate_fn(A_						)
           _lowerCamelCase			    =				jax.tree_util.tree_map(A_				,				A_						)
           return batch



     def 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.fetch_inputs(features['''input_ids''']						)
           _lowerCamelCase			    =				{
               '''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 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[self._fetch_inputs(A_						) for ids in input_ids]
           return zip(*A_						)





     def 		UpperCamelCase_						(					self				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				[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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None		)						->    Union[str, Any]:



      '''simple docstring'''

      if seed is not None:
            _lowerCamelCase			    =				dataset.shuffle(seed=__UpperCAmelCase		)
      for i in range(len(__UpperCAmelCase		) // batch_size		):
            _lowerCamelCase			    =				dataset[i * batch_size : (i + 1) * batch_size]
            yield dict(__UpperCAmelCase		)
@partial(jax.pmap ,						axis_name='''batch'''		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						**__UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      def loss_fn(__UpperCAmelCase		):
            _lowerCamelCase			    =				model_inputs.pop('''start_labels'''		)
            _lowerCamelCase			    =				model_inputs.pop('''end_labels'''		)
            _lowerCamelCase			    =				model_inputs.pop('''pooled_labels'''		)

            _lowerCamelCase			    =				state.apply_fn(**__UpperCAmelCase ,						params=__UpperCAmelCase ,						dropout_rng=__UpperCAmelCase ,						train=__UpperCAmelCase		)
            _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				outputs

            return state.loss_fn(
                __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						)

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

      _lowerCamelCase			    =				state.apply_gradients(grads=__UpperCAmelCase		)
      return state, metrics, new_drp_rng
@partial(jax.pmap ,						axis_name='''batch'''		)
def       __magic_name__(  __UpperCAmelCase ,						**__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

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

      _lowerCamelCase			    =				state.apply_fn(**__UpperCAmelCase ,						params=state.params ,						train=__UpperCAmelCase		)
      _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				outputs

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



class  UpperCamelCase				( train_state.TrainState ):

     '''simple docstring'''

     A_										=  struct.field(pytree_node=__lowercase )





@dataclass
class  UpperCamelCase				:

     '''simple docstring'''

     A_										=  42
     A_										=  42
     A_										=  42
     A_										=  42
     A_										=  42
     A_										=  42
     A_										=  None



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_=None						) ->				List[str]:






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



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.args
           _lowerCamelCase			    =				len(A_						) // args.batch_size

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

                             _lowerCamelCase			    =				self.evaluate(A_				,				A_						)
                             _lowerCamelCase			    =				{
                                 '''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 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Optional[int]:






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





     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    List[Any]:



      '''simple docstring'''

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

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

      _lowerCamelCase			    =				joblib.load(os.path.join(__UpperCAmelCase ,						'''args.joblib'''		)		)
      _lowerCamelCase			    =				joblib.load(os.path.join(__UpperCAmelCase ,						'''data_collator.joblib'''		)		)

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

      print('''DONE'''		)
      return params, opt_state, step, args, data_collator
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Union[str, Any]:



      '''simple docstring'''

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




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

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

      _lowerCamelCase			    =				scheduler_fn(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)

      _lowerCamelCase			    =				optax.adamw(learning_rate=__UpperCAmelCase ,						weight_decay=__UpperCAmelCase ,						mask=__UpperCAmelCase		)
      return tx, lr
import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property



@unittest.skipUnless(os.path.exists(__lowercase )   ,  'Tatoeba directory does not exist.' )
class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     @cached_property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				tempfile.mkdtemp()
           return TatoebaConverter(save_dir=A_						)



     @slow
     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           self.resolver.convert_models(['''heb-eng''']						)





     @slow
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self.resolver.write_model_card('''opus-mt-he-en'''				,				dry_run=A_						)
           assert mmeta["long_pair"] == "heb-eng"