

"""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"""







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


 def  _lowercase			(      self							:      List[str]     ):
    super().setup()
    __lowercase								=						nn.Dense(5, dtype=self.dtype     )



 def __call__(      self							:      Tuple, *UpperCAmelCase__							:      int, **UpperCAmelCase__							:      Any     ):
    __lowercase								=						super().__call__(*UpperCAmelCase__, **UpperCAmelCase__     )
    __lowercase								=						self.cls(outputs[2]     )
    return outputs[:2] + (cls_out,)







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : List[Any]       		=     FlaxBigBirdForNaturalQuestionsModule







def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	str,      UpperCamelCase_					:	List[str])     ->						str:
   '''simple docstring'''


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

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




@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase     : str       		=     "google/bigbird-roberta-base"
 __UpperCAmelCase     : int       		=     3_0_0_0
 __UpperCAmelCase     : int       		=     1_0_5_0_0

 __UpperCAmelCase     : int       		=     1_2_8
 __UpperCAmelCase     : int       		=     3

 __UpperCAmelCase     : int       		=     1
 __UpperCAmelCase     : int       		=     5

 # tx_args
 __UpperCAmelCase     : float       		=     3E-5
 __UpperCAmelCase     : float       		=     0.0
 __UpperCAmelCase     : int       		=     2_0_0_0_0
 __UpperCAmelCase     : float       		=     0.0_095

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


 def  _lowercase			(      self							:      Union[str, Any]     ):
    os.makedirs(self.base_dir, exist_ok=UpperCAmelCase__     )
    __lowercase								=						os.path.join(self.base_dir, self.save_dir     )
    __lowercase								=						self.batch_size_per_device * jax.device_count()







@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase     : int
 __UpperCAmelCase     : int       		=     4_0_9_6  # no dynamic padding on TPUs


 def __call__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any     ):
    __lowercase								=						self.collate_fn(UpperCAmelCase__     )
    __lowercase								=						jax.tree_util.tree_map(UpperCAmelCase__, UpperCAmelCase__     )
    return batch


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      Any     ):
    __lowercase		,__lowercase								=						self.fetch_inputs(features["input_ids"]     )
    __lowercase								=						{
        "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  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      list     ):
    __lowercase								=						[self._fetch_inputs(UpperCAmelCase__     ) for ids in input_ids]
    return zip(*UpperCAmelCase__     )



 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      list     ):
    __lowercase								=						[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   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Optional[Any]=None)     ->						Tuple:
   '''simple docstring'''


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




@partial(jax.pmap,      axis_name="batch")
def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Union[str, Any],      **UpperCamelCase_					:	List[str])     ->						Tuple:
   '''simple docstring'''


   def loss_fn(UpperCamelCase_					:	Tuple):
      __lowercase								=						model_inputs.pop("start_labels")
      __lowercase								=						model_inputs.pop("end_labels")
      __lowercase								=						model_inputs.pop("pooled_labels")

      __lowercase								=						state.apply_fn(**UpperCamelCase_,      params=UpperCamelCase_,      dropout_rng=UpperCamelCase_,      train=UpperCamelCase_)
      __lowercase		,__lowercase		,__lowercase								=						outputs

      return state.loss_fn(
          UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      )

   __lowercase		,__lowercase								=						jax.random.split(UpperCamelCase_)
   __lowercase								=						jax.value_and_grad(UpperCamelCase_)
   __lowercase		,__lowercase								=						grad_fn(state.params)
   __lowercase								=						jax.lax.pmean({"loss": loss},      axis_name="batch")
   __lowercase								=						jax.lax.pmean(UpperCamelCase_,      "batch")

   __lowercase								=						state.apply_gradients(grads=UpperCamelCase_)
   return state, metrics, new_drp_rng




@partial(jax.pmap,      axis_name="batch")
def   _A  (  UpperCamelCase_					:	Any,      **UpperCamelCase_					:	Optional[Any])     ->						List[str]:
   '''simple docstring'''


   __lowercase								=						model_inputs.pop("start_labels")
   __lowercase								=						model_inputs.pop("end_labels")
   __lowercase								=						model_inputs.pop("pooled_labels")

   __lowercase								=						state.apply_fn(**UpperCamelCase_,      params=state.params,      train=UpperCamelCase_)
   __lowercase		,__lowercase		,__lowercase								=						outputs

   __lowercase								=						state.loss_fn(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
   __lowercase								=						jax.lax.pmean({"loss": loss},      axis_name="batch")
   return metrics




class  _lowerCAmelCase  (     train_state.TrainState       ):







 """simple docstring"""







 __UpperCAmelCase     : Callable       		=     struct.field(pytree_node=lowercase       )







@dataclass
class  _lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase     : Args
 __UpperCAmelCase     : Callable
 __UpperCAmelCase     : Callable
 __UpperCAmelCase     : Callable
 __UpperCAmelCase     : Callable
 __UpperCAmelCase     : wandb
 __UpperCAmelCase     : Callable       		=     None


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      List[Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      Tuple=None     ):
    __lowercase								=						model.params
    __lowercase								=						TrainState.create(
        apply_fn=model.__call__, params=UpperCAmelCase__, tx=UpperCAmelCase__, loss_fn=UpperCAmelCase__, )
    if ckpt_dir is not None:
       __lowercase		,__lowercase		,__lowercase		,__lowercase		,__lowercase								=						restore_checkpoint(UpperCAmelCase__, UpperCAmelCase__     )
       __lowercase								=						{
           "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								=						build_tx(**UpperCAmelCase__     )
       __lowercase								=						train_state.TrainState(
           step=UpperCAmelCase__, apply_fn=model.__call__, params=UpperCAmelCase__, tx=UpperCAmelCase__, opt_state=UpperCAmelCase__, )
       __lowercase								=						args
       __lowercase								=						data_collator
       __lowercase								=						lr
       __lowercase								=						params
    __lowercase								=						jax_utils.replicate(UpperCAmelCase__     )
    return state


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Tuple     ):
    __lowercase								=						self.args
    __lowercase								=						len(UpperCAmelCase__     ) // args.batch_size

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

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



 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      str     ):
    __lowercase								=						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   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Union[str, Any])     ->						List[Any]:
   '''simple docstring'''


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

   with open(os.path.join(UpperCamelCase_,      "opt_state.msgpack"),      "rb") as f:
      __lowercase								=						from_bytes(state.opt_state,      f.read())

   __lowercase								=						joblib.load(os.path.join(UpperCamelCase_,      "args.joblib"))
   __lowercase								=						joblib.load(os.path.join(UpperCamelCase_,      "data_collator.joblib"))

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

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




def   _A  (  UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	str,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Optional[int])     ->						Optional[int]:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Any,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


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

   __lowercase								=						scheduler_fn(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)

   __lowercase								=						optax.adamw(learning_rate=UpperCamelCase_,      weight_decay=UpperCamelCase_,      mask=UpperCamelCase_)
   return tx, lr







"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_tensor(
        [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-4     )     )




