
import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :					Optional[Any]						    =							logging.get_logger(__name__)

lowercase  :					List[Any]						    =							{
    """facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
    """facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						int=				"encodec"
    def __init__(  self		,snake_case=[1.5, 3.0, 6.0, 12.0, 24.0]		,snake_case=24000		,snake_case=1		,snake_case=False		,snake_case=None		,snake_case=None		,snake_case=128		,snake_case=32		,snake_case=1		,snake_case=[8, 5, 4, 2]		,snake_case="weight_norm"		,snake_case=7		,snake_case=7		,snake_case=3		,snake_case=2		,snake_case=True		,snake_case="reflect"		,snake_case=2		,snake_case=2		,snake_case=1.0		,snake_case=1024		,snake_case=None		,snake_case=True		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      target_bandwidths
           lowercase							:     int       	=      sampling_rate
           lowercase							:     List[str]       	=      audio_channels
           lowercase							:     Tuple       	=      normalize
           lowercase							:     Optional[Any]       	=      chunk_length_s
           lowercase							:     List[str]       	=      overlap
           lowercase							:     List[Any]       	=      hidden_size
           lowercase							:     Tuple       	=      num_filters
           lowercase							:     Dict       	=      num_residual_layers
           lowercase							:     str       	=      upsampling_ratios
           lowercase							:     str       	=      norm_type
           lowercase							:     List[Any]       	=      kernel_size
           lowercase							:     Tuple       	=      last_kernel_size
           lowercase							:     Any       	=      residual_kernel_size
           lowercase							:     Union[str, Any]       	=      dilation_growth_rate
           lowercase							:     Union[str, Any]       	=      use_causal_conv
           lowercase							:     int       	=      pad_mode
           lowercase							:     List[str]       	=      compress
           lowercase							:     List[str]       	=      num_lstm_layers
           lowercase							:     Union[str, Any]       	=      trim_right_ratio
           lowercase							:     List[Any]       	=      codebook_size
           lowercase							:     List[Any]       	=      codebook_dim if codebook_dim is not None else hidden_size
           lowercase							:     Optional[int]       	=      use_conv_shortcut

           if self.norm_type not in ["weight_norm", "time_group_norm"]:
                  raise ValueError(
                      f"self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"      )

           super().__init__(**snake_case      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.chunk_length_s is None:
                  return None
           else:
                  return int(self.chunk_length_s * self.sampling_rate      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.chunk_length_s is None or self.overlap is None:
                  return None
           else:
                  return max(1		,int((1.0 - self.overlap) * self.chunk_length      )      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      np.prod(self.upsampling_ratios      )
           return math.ceil(self.sampling_rate / hop_length      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)      )


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 __snake_case		(      lowerCAmelCase     ):
    _a		:						BigBirdConfig
    _a		:						jnp.dtype=				jnp.floataa
    _a		:						bool=				True
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           super().setup()
           lowercase							:     List[str]       	=      nn.Dense(5		,dtype=self.dtype      )
    def __call__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      super().__call__(*snake_case		,**snake_case      )
           lowercase							:     Any       	=      self.cls(outputs[2]      )
           return outputs[:2] + (cls_out,)






class __snake_case		(      lowerCAmelCase     ):
    _a		:						List[Any]=				FlaxBigBirdForNaturalQuestionsModule







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       def cross_entropy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				):
              lowercase							:     int       	=      logits.shape[-1]
              lowercase							:     Dict       	=      (labels[..., None] == jnp.arange(SCREAMING_SNAKE_CASE__				)[None]).astype("""f4"""				)
              lowercase							:     Any       	=      jax.nn.log_softmax(SCREAMING_SNAKE_CASE__					,	axis=-1				)
              lowercase							:     Optional[Any]       	=      -jnp.sum(labels * logits					,	axis=-1				)
              if reduction is not None:
                     lowercase							:     Any       	=      reduction(SCREAMING_SNAKE_CASE__				)
              return loss

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

@dataclass
class __snake_case		:
    _a		:						str=				"google/bigbird-roberta-base"
    _a		:						int=				3000
    _a		:						int=				1_0500

    _a		:						int=				128
    _a		:						int=				3

    _a		:						int=				1
    _a		:						int=				5

    # tx_args
    _a		:						float=				3E-5
    _a		:						float=				0.0
    _a		:						int=				2_0000
    _a		:						float=				0.00_95

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



           '''simple docstring'''







           os.makedirs(self.base_dir		,exist_ok=snake_case      )
           lowercase							:     Optional[int]       	=      os.path.join(self.base_dir		,self.save_dir      )
           lowercase							:     Optional[int]       	=      self.batch_size_per_device * jax.device_count()






@dataclass
class __snake_case		:
    _a		:						int
    _a		:						int=				4096  # no dynamic padding on TPUs
    def __call__(  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.collate_fn(snake_case      )
           lowercase							:     Union[str, Any]       	=      jax.tree_util.tree_map(snake_case		,snake_case      )
           return batch
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Union[str, Any]       	=      self.fetch_inputs(features["""input_ids"""]      )
           lowercase							:     Tuple       	=      {
               """input_ids""": jnp.array(snake_case		,dtype=jnp.intaa      ),
               """attention_mask""": jnp.array(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       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      [self._fetch_inputs(snake_case      ) for ids in input_ids]
           return zip(*snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Union[str, Any]       	=      [1 for _ in range(len(snake_case      )      )]
           while len(snake_case      ) < self.max_length:
                  input_ids.append(self.pad_id      )
                  attention_mask.append(0      )
           return input_ids, attention_mask







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				) ->    Any:
       if seed is not None:
              lowercase							:     Optional[int]       	=      dataset.shuffle(seed=SCREAMING_SNAKE_CASE__				)
       for i in range(len(SCREAMING_SNAKE_CASE__				) // batch_size				):
              lowercase							:     Optional[Any]       	=      dataset[i * batch_size : (i + 1) * batch_size]
              yield dict(SCREAMING_SNAKE_CASE__				)







@partial(jax.pmap					,	axis_name="""batch"""				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       def loss_fn(SCREAMING_SNAKE_CASE__				):
              lowercase							:     List[str]       	=      model_inputs.pop("""start_labels"""				)
              lowercase							:     Optional[int]       	=      model_inputs.pop("""end_labels"""				)
              lowercase							:     str       	=      model_inputs.pop("""pooled_labels"""				)

              lowercase							:     Union[str, Any]       	=      state.apply_fn(**SCREAMING_SNAKE_CASE__					,	params=SCREAMING_SNAKE_CASE__					,	dropout_rng=SCREAMING_SNAKE_CASE__					,	train=SCREAMING_SNAKE_CASE__				)
              lowercase	,			lowercase	,			lowercase							:     List[str]       	=      outputs

              return state.loss_fn(
                  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	)

       lowercase	,			lowercase							:     int       	=      jax.random.split(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Dict       	=      jax.value_and_grad(SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase							:     Union[str, Any]       	=      grad_fn(state.params				)
       lowercase							:     List[Any]       	=      jax.lax.pmean({"""loss""": loss}					,	axis_name="""batch"""				)
       lowercase							:     List[Any]       	=      jax.lax.pmean(SCREAMING_SNAKE_CASE__					,	"""batch"""				)

       lowercase							:     str       	=      state.apply_gradients(grads=SCREAMING_SNAKE_CASE__				)
       return state, metrics, new_drp_rng







@partial(jax.pmap					,	axis_name="""batch"""				)
def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	**SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     int       	=      model_inputs.pop("""start_labels"""				)
       lowercase							:     Dict       	=      model_inputs.pop("""end_labels"""				)
       lowercase							:     Optional[Any]       	=      model_inputs.pop("""pooled_labels"""				)

       lowercase							:     Optional[int]       	=      state.apply_fn(**SCREAMING_SNAKE_CASE__					,	params=state.params					,	train=SCREAMING_SNAKE_CASE__				)
       lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      outputs

       lowercase							:     Dict       	=      state.loss_fn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      jax.lax.pmean({"""loss""": loss}					,	axis_name="""batch"""				)
       return metrics

class __snake_case		(      train_state.TrainState     ):
    _a		:						Callable=				struct.field(pytree_node=lowerCAmelCase     )






@dataclass
class __snake_case		:
    _a		:						Args
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						Callable
    _a		:						wandb
    _a		:						Callable=				None
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      model.params
           lowercase							:     Any       	=      TrainState.create(
               apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,loss_fn=snake_case		,)
           if ckpt_dir is not None:
                  lowercase	,			lowercase	,			lowercase	,			lowercase	,			lowercase							:     Tuple       	=      restore_checkpoint(snake_case		,snake_case      )
                  lowercase							:     List[str]       	=      {
                      """lr""": args.lr,
                      """init_lr""": args.init_lr,
                      """warmup_steps""": args.warmup_steps,
                      """num_train_steps""": num_train_steps,
                      """weight_decay""": args.weight_decay,
                  }
                  lowercase	,			lowercase							:     Tuple       	=      build_tx(**snake_case      )
                  lowercase							:     str       	=      train_state.TrainState(
                      step=snake_case		,apply_fn=model.__call__		,params=snake_case		,tx=snake_case		,opt_state=snake_case		,)
                  lowercase							:     Any       	=      args
                  lowercase							:     Optional[Any]       	=      data_collator
                  lowercase							:     List[str]       	=      lr
                  lowercase							:     str       	=      params
           lowercase							:     Tuple       	=      jax_utils.replicate(snake_case      )
           return state
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Dict       	=      self.args
           lowercase							:     Optional[Any]       	=      len(snake_case      ) // args.batch_size

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

                                lowercase							:     int       	=      self.evaluate(snake_case		,snake_case      )
                                lowercase							:     Tuple       	=      {
                                    """step""": state_step.item(),
                                    """eval_loss""": eval_loss.item(),
                                    """tr_loss""": tr_loss,
                                    """lr""": lr.item(),
                                }
                                tqdm.write(str(snake_case      )      )
                                self.logger.log(snake_case		,commit=snake_case      )

                         if i % args.save_steps == 0:
                                self.save_checkpoint(args.save_dir + f"-e{epoch}-s{i}"		,state=snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     str       	=      jax_utils.unreplicate(snake_case      )
           print(f"SAVING CHECKPOINT IN {save_dir}"		,end=""" ... """      )
           self.model_save_fn(snake_case		,params=state.params      )
           with open(os.path.join(snake_case		,"""opt_state.msgpack"""      )		,"""wb"""      ) as f:
                  f.write(to_bytes(state.opt_state      )      )
           joblib.dump(self.args		,os.path.join(snake_case		,"""args.joblib"""      )      )
           joblib.dump(self.data_collator		,os.path.join(snake_case		,"""data_collator.joblib"""      )      )
           with open(os.path.join(snake_case		,"""training_state.json"""      )		,"""w"""      ) as f:
                  json.dump({"""step""": state.step.item()}		,snake_case      )
           print("""DONE"""      )







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       print(f"RESTORING CHECKPOINT FROM {save_dir}"					,	end=""" ... """				)
       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""flax_model.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     str       	=      from_bytes(state.params					,	f.read()				)

       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""opt_state.msgpack"""				)					,	"""rb"""				) as f:
              lowercase							:     Optional[int]       	=      from_bytes(state.opt_state					,	f.read()				)

       lowercase							:     Optional[Any]       	=      joblib.load(os.path.join(SCREAMING_SNAKE_CASE__					,	"""args.joblib"""				)				)
       lowercase							:     int       	=      joblib.load(os.path.join(SCREAMING_SNAKE_CASE__					,	"""data_collator.joblib"""				)				)

       with open(os.path.join(SCREAMING_SNAKE_CASE__					,	"""training_state.json"""				)					,	"""r"""				) as f:
              lowercase							:     Tuple       	=      json.load(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      training_state["""step"""]

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







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       lowercase							:     List[str]       	=      num_train_steps - warmup_steps
       lowercase							:     Dict       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=SCREAMING_SNAKE_CASE__					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      optax.linear_schedule(init_value=SCREAMING_SNAKE_CASE__					,	end_value=1e-7					,	transition_steps=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Tuple       	=      optax.join_schedules(schedules=[warmup_fn, decay_fn]					,	boundaries=[warmup_steps]				)
       return lr







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       def weight_decay_mask(SCREAMING_SNAKE_CASE__				):
              lowercase							:     List[Any]       	=      traverse_util.flatten_dict(SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[Any]       	=      {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
              return traverse_util.unflatten_dict(SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      scheduler_fn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      optax.adamw(learning_rate=SCREAMING_SNAKE_CASE__					,	weight_decay=SCREAMING_SNAKE_CASE__					,	mask=SCREAMING_SNAKE_CASE__				)
       return tx, lr
