





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     ):
      a						       =							42
      a						       =							jnp.floataa
      a						       =							True
      def 		_lowerCamelCase      (		self       ):
         super().setup()
         A_     :				Tuple			      =	nn.Dense(5      ,       dtype=self.dtype       )






      def __call__(		self      ,       *a__      ,       **a__       ):
         A_     :				Optional[int]			      =	super().__call__(*UpperCAmelCase__      ,       **UpperCAmelCase__       )
         A_     :				Union[str, Any]			      =	self.cls(outputs[2]       )
         return outputs[:2] + (cls_out,)






class    _UpperCAmelCase							(		_lowerCamelCase     ):
      a						       =							FlaxBigBirdForNaturalQuestionsModule






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   def cross_entropy(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=None     ):
      A_     :				Dict			      =	logits.shape[-1]
      A_     :				List[str]			      =	(labels[..., None] == jnp.arange(_lowerCAmelCase     )[None]).astype("""f4"""     )
      A_     :				List[str]			      =	jax.nn.log_softmax(_lowerCAmelCase  ,axis=-1     )
      A_     :				Tuple			      =	-jnp.sum(labels * logits  ,axis=-1     )
      if reduction is not None:
         A_     :				Tuple			      =	reduction(_lowerCAmelCase     )
      return loss

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

@dataclass
class    _UpperCAmelCase							:
      a						       =							"google/bigbird-roberta-base"
      a						       =							3000
      a						       =							1_0500

      a						       =							128
      a						       =							3

      a						       =							1
      a						       =							5

      # tx_args
      a						       =							3e-5
      a						       =							0.0
      a						       =							2_0000
      a						       =							0.0095

      a						       =							"bigbird-roberta-natural-questions"
      a						       =							"training-expt"
      a						       =							"data/nq-training.jsonl"
      a						       =							"data/nq-validation.jsonl"
      def 		_lowerCamelCase      (		self       ):
         os.makedirs(self.base_dir      ,       exist_ok=UpperCAmelCase__       )
         A_     :				str			      =	os.path.join(self.base_dir      ,       self.save_dir       )
         A_     :				Any			      =	self.batch_size_per_device * jax.device_count()






@dataclass
class    _UpperCAmelCase							:
      a						       =							42
      a						       =							4096  # no dynamic padding on TPUs
      def __call__(		self      ,       a__       ):
         A_     :				Optional[Any]			      =	self.collate_fn(UpperCAmelCase__       )
         A_     :				List[Any]			      =	jax.tree_util.tree_map(UpperCAmelCase__      ,       UpperCAmelCase__       )
         return batch
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				str			      =	self.fetch_inputs(features["""input_ids"""]       )
         A_     :				List[str]			      =	{
             '''input_ids''': jnp.array(UpperCAmelCase__      ,       dtype=jnp.intaa       ),
             '''attention_mask''': jnp.array(UpperCAmelCase__      ,       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      ,       a__       ):
         A_     :				str			      =	[self._fetch_inputs(UpperCAmelCase__       ) for ids in input_ids]
         return zip(*UpperCAmelCase__       )






      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[Any]			      =	[1 for _ in range(len(UpperCAmelCase__       )       )]
         while len(UpperCAmelCase__       ) < self.max_length:
            input_ids.append(self.pad_id       )
            attention_mask.append(0       )
         return input_ids, attention_mask






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase=None     ):
   '''simple docstring'''


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



@partial(jax.pmap  ,axis_name="""batch"""     )
def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,**_lowerCAmelCase     ):
   '''simple docstring'''


   def loss_fn(_lowerCAmelCase     ):
      A_     :				int			      =	model_inputs.pop("""start_labels"""     )
      A_     :				Any			      =	model_inputs.pop("""end_labels"""     )
      A_     :				str			      =	model_inputs.pop("""pooled_labels"""     )

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

      return state.loss_fn(
          _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,)

   A_     :				Any			      =	jax.random.split(_lowerCAmelCase     )
   A_     :				Tuple			      =	jax.value_and_grad(_lowerCAmelCase     )
   A_     :				List[Any]			      =	grad_fn(state.params     )
   A_     :				str			      =	jax.lax.pmean({"""loss""": loss}  ,axis_name="""batch"""     )
   A_     :				Optional[Any]			      =	jax.lax.pmean(_lowerCAmelCase  ,"""batch"""     )

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



@partial(jax.pmap  ,axis_name="""batch"""     )
def     _lowerCAmelCase		(  _lowerCAmelCase  ,**_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	model_inputs.pop("""start_labels"""     )
   A_     :				int			      =	model_inputs.pop("""end_labels"""     )
   A_     :				Optional[int]			      =	model_inputs.pop("""pooled_labels"""     )

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

   A_     :				Any			      =	state.loss_fn(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )
   A_     :				Any			      =	jax.lax.pmean({"""loss""": loss}  ,axis_name="""batch"""     )
   return metrics

class    _UpperCAmelCase							(		train_state.TrainState     ):
      a						       =							struct.field(pytree_node=_lowerCamelCase     )






@dataclass
class    _UpperCAmelCase							:
      a						       =							42
      a						       =							42
      a						       =							42
      a						       =							42
      a						       =							42
      a						       =							42
      a						       =							None
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__      ,       a__=None       ):
         A_     :				str			      =	model.params
         A_     :				Union[str, Any]			      =	TrainState.create(
             apply_fn=model.__call__      ,       params=UpperCAmelCase__      ,       tx=UpperCAmelCase__      ,       loss_fn=UpperCAmelCase__      ,       )
         if ckpt_dir is not None:
            A_     :				Any			      =	restore_checkpoint(UpperCAmelCase__      ,       UpperCAmelCase__       )
            A_     :				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,
            }
            A_     :				int			      =	build_tx(**UpperCAmelCase__       )
            A_     :				str			      =	train_state.TrainState(
                step=UpperCAmelCase__      ,       apply_fn=model.__call__      ,       params=UpperCAmelCase__      ,       tx=UpperCAmelCase__      ,       opt_state=UpperCAmelCase__      ,       )
            A_     :				List[Any]			      =	args
            A_     :				List[Any]			      =	data_collator
            A_     :				Optional[Any]			      =	lr
            A_     :				Union[str, Any]			      =	params
         A_     :				Union[str, Any]			      =	jax_utils.replicate(UpperCAmelCase__       )
         return state
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
         A_     :				int			      =	self.args
         A_     :				List[Any]			      =	len(UpperCAmelCase__       ) // args.batch_size

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

                  A_     :				List[str]			      =	self.evaluate(UpperCAmelCase__      ,       UpperCAmelCase__       )
                  A_     :				Optional[int]			      =	{
                      '''step''': state_step.item(),
                      '''eval_loss''': eval_loss.item(),
                      '''tr_loss''': tr_loss,
                      '''lr''': lr.item(),
                  }
                  tqdm.write(str(UpperCAmelCase__       )       )
                  self.logger.log(UpperCAmelCase__      ,       commit=UpperCAmelCase__       )

               if i % args.save_steps == 0:
                  self.save_checkpoint(args.save_dir + F"""-e{epoch}-s{i}"""      ,       state=UpperCAmelCase__       )
      def 		_lowerCamelCase      (		self      ,       a__      ,       a__       ):
         A_     :				Optional[int]			      =	get_batched_dataset(UpperCAmelCase__      ,       self.args.batch_size       )
         A_     :				List[Any]			      =	len(UpperCAmelCase__       ) // self.args.batch_size
         A_     :				str			      =	jnp.array(0      ,       dtype=jnp.floataa       )
         A_     :				Union[str, Any]			      =	0
         for batch in tqdm(UpperCAmelCase__      ,       total=UpperCAmelCase__      ,       desc="""Evaluating ... """       ):
            A_     :				int			      =	self.data_collator(UpperCAmelCase__       )
            A_     :				Any			      =	self.val_step_fn(UpperCAmelCase__      ,       **UpperCAmelCase__       )
            running_loss += jax_utils.unreplicate(metrics["""loss"""]       )
            i += 1
         return running_loss / i






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






def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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

   with open(os.path.join(_lowerCAmelCase  ,"""opt_state.msgpack"""     )  ,"""rb"""     ) as f:
      A_     :				Optional[Any]			      =	from_bytes(state.opt_state  ,f.read()     )

   A_     :				Tuple			      =	joblib.load(os.path.join(_lowerCAmelCase  ,"""args.joblib"""     )     )
   A_     :				Optional[int]			      =	joblib.load(os.path.join(_lowerCAmelCase  ,"""data_collator.joblib"""     )     )

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

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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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







def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


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

   A_     :				int			      =	scheduler_fn(_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     )

   A_     :				str			      =	optax.adamw(learning_rate=_lowerCAmelCase  ,weight_decay=_lowerCAmelCase  ,mask=_lowerCAmelCase     )
   return tx, lr













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


_lowerCAmelCase 				=     {
    """configuration_speecht5""": [
        """SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP""",
        """SpeechT5Config""",
        """SpeechT5HifiGanConfig""",
    ],
    """feature_extraction_speecht5""": ["""SpeechT5FeatureExtractor"""],
    """processing_speecht5""": ["""SpeechT5Processor"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     ["""SpeechT5Tokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SpeechT5ForSpeechToText""",
          """SpeechT5ForSpeechToSpeech""",
          """SpeechT5ForTextToSpeech""",
          """SpeechT5Model""",
          """SpeechT5PreTrainedModel""",
          """SpeechT5HifiGan""",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






