





"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
           TFDebertaVaModel,
       )






class 		__snake_case					:





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase=13					,						__lowerCamelCase=7					,						__lowerCamelCase=True					,						__lowerCamelCase=True					,						__lowerCamelCase=True					,						__lowerCamelCase=True					,						__lowerCamelCase=99					,						__lowerCamelCase=32					,						__lowerCamelCase=2					,						__lowerCamelCase=4					,						__lowerCamelCase=37					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=16					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=False					,						__lowerCamelCase=True					,						__lowerCamelCase="None"					,						__lowerCamelCase=3					,						__lowerCamelCase=4					,						__lowerCamelCase=None					,						):



     '''simple docstring'''




     __A    :						Dict						 =     parent
     __A    :						List[str]						 =     batch_size
     __A    :						Any						 =     seq_length
     __A    :						List[str]						 =     is_training
     __A    :						List[str]						 =     use_input_mask
     __A    :						Tuple						 =     use_token_type_ids
     __A    :						List[str]						 =     use_labels
     __A    :						List[str]						 =     vocab_size
     __A    :						List[str]						 =     hidden_size
     __A    :						Tuple						 =     num_hidden_layers
     __A    :						str						 =     num_attention_heads
     __A    :						List[Any]						 =     intermediate_size
     __A    :						Tuple						 =     hidden_act
     __A    :						Tuple						 =     hidden_dropout_prob
     __A    :						Any						 =     attention_probs_dropout_prob
     __A    :						str						 =     max_position_embeddings
     __A    :						Optional[Any]						 =     type_vocab_size
     __A    :						Union[str, Any]						 =     type_sequence_label_size
     __A    :						Optional[int]						 =     initializer_range
     __A    :						int						 =     num_labels
     __A    :						Union[str, Any]						 =     num_choices
     __A    :						List[str]						 =     relative_attention
     __A    :						List[str]						 =     position_biased_input
     __A    :						int						 =     pos_att_type
     __A    :						int						 =     scope

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size  )

     __A    :						Optional[int]						 =     None
     if self.use_input_mask:
         __A    :						Optional[Any]						 =     random_attention_mask([self.batch_size, self.seq_length]  )

     __A    :						Union[str, Any]						 =     None
     if self.use_token_type_ids:
         __A    :						Optional[Any]						 =     ids_tensor([self.batch_size, self.seq_length]					,						self.type_vocab_size  )

     __A    :						Optional[int]						 =     None
     __A    :						Any						 =     None
     __A    :						Tuple						 =     None
     if self.use_labels:
         __A    :						List[str]						 =     ids_tensor([self.batch_size]					,						self.type_sequence_label_size  )
         __A    :						Optional[Any]						 =     ids_tensor([self.batch_size, self.seq_length]					,						self.num_labels  )

     __A    :						Dict						 =     DebertaVaConfig(
         vocab_size=self.vocab_size					,						hidden_size=self.hidden_size					,						num_hidden_layers=self.num_hidden_layers					,						num_attention_heads=self.num_attention_heads					,						intermediate_size=self.intermediate_size					,						hidden_act=self.hidden_act					,						hidden_dropout_prob=self.hidden_dropout_prob					,						attention_probs_dropout_prob=self.attention_probs_dropout_prob					,						max_position_embeddings=self.max_position_embeddings					,						type_vocab_size=self.type_vocab_size					,						relative_attention=self.relative_attention					,						position_biased_input=self.position_biased_input					,						initializer_range=self.initializer_range					,						return_dict=__lowerCamelCase					,						)

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     TFDebertaVaModel(config=__lowerCamelCase  )
     __A    :						str						 =     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

     __A    :						Optional[Any]						 =     [input_ids, input_mask]
     __A    :						int						 =     model(__lowerCamelCase  )

     __A    :						str						 =     model(__lowerCamelCase  )

     self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						str						 =     TFDebertaVaForMaskedLM(config=__lowerCamelCase  )
     __A    :						Union[str, Any]						 =     {
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }
     __A    :						Union[str, Any]						 =     model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.num_labels
     __A    :						Optional[int]						 =     TFDebertaVaForSequenceClassification(config=__lowerCamelCase  )
     __A    :						Any						 =     {
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }

     __A    :						Optional[Any]						 =     model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Tuple						 =     self.num_labels
     __A    :						Any						 =     TFDebertaVaForTokenClassification(config=__lowerCamelCase  )
     __A    :						Union[str, Any]						 =     {
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }
     __A    :						Tuple						 =     model(__lowerCamelCase  )
     self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels)  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						int						 =     TFDebertaVaForQuestionAnswering(config=__lowerCamelCase  )
     __A    :						int						 =     {
         '''input_ids''': input_ids,
         '''attention_mask''': input_mask,
         '''token_type_ids''': token_type_ids,
     }

     __A    :						Optional[Any]						 =     model(__lowerCamelCase  )
     self.parent.assertEqual(result.start_logits.shape					,						(self.batch_size, self.seq_length)  )
     self.parent.assertEqual(result.end_logits.shape					,						(self.batch_size, self.seq_length)  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						int						 =     self.prepare_config_and_inputs()
     (
         (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       (
         __A
     ) ,       
     )    :						str						 =     config_and_inputs
     __A    :						Union[str, Any]						 =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
     return config, inputs_dict






@require_tf
class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





 """simple docstring"""


 _lowerCamelCase        =     (
     (
         TFDebertaVaModel,
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 _lowerCamelCase        =     (
     {
         """feature-extraction""": TFDebertaVaModel,
         """fill-mask""": TFDebertaVaForMaskedLM,
         """question-answering""": TFDebertaVaForQuestionAnswering,
         """text-classification""": TFDebertaVaForSequenceClassification,
         """token-classification""": TFDebertaVaForTokenClassification,
         """zero-shot""": TFDebertaVaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 _lowerCamelCase        =     False
 _lowerCamelCase        =     False

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Tuple						 =     TFDebertaVaModelTester(self  )
     __A    :						str						 =     ConfigTester(self					,						config_class=__lowerCamelCase					,						hidden_size=37  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self.config_tester.run_common_tests()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Dict						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Union[str, Any]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__lowerCamelCase  )







 @slow
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''  )
     self.assertIsNotNone(__lowerCamelCase  )






@require_tf
class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 @unittest.skip(reason='''Model not available yet'''  )
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     pass







 @slow
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						str						 =     TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''  )
     __A    :						Optional[int]						 =     tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]  )
     __A    :						Tuple						 =     tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
     __A    :						List[Any]						 =     model(__lowerCamelCase					,						attention_mask=__lowerCamelCase  )[0]

     __A    :						List[str]						 =     tf.constant(
         [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]  )
     tf.debugging.assert_near(output[:, 1:4, 1:4]					,						__lowerCamelCase					,						atol=1e-4  )











"""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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     jnp.floataa
 _lowerCamelCase        =     True

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     super().setup()
     __A    :						List[Any]						 =     nn.Dense(5					,						dtype=self.dtype  )







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



     '''simple docstring'''




     __A    :						Tuple						 =     super().__call__(*__lowerCamelCase					,						**__lowerCamelCase  )
     __A    :						Optional[int]						 =     self.cls(outputs[2]  )
     return outputs[:2] + (cls_out,)






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     FlaxBigBirdForNaturalQuestionsModule







def 		__lowercase     (   snake_case_  :  Tuple				,snake_case_  :  Optional[int]				,snake_case_  :  int				,snake_case_  :  Tuple				,snake_case_  :  Any				,snake_case_  :  Any						)   ->List[Any]:



    '''simple docstring'''







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

    __A    :						str						 =     partial(snake_case_				,reduction=jnp.mean						)
    __A    :						Dict						 =     cross_entropy(snake_case_				,snake_case_						)
    __A    :						List[str]						 =     cross_entropy(snake_case_				,snake_case_						)
    __A    :						str						 =     cross_entropy(snake_case_				,snake_case_						)
    return (start_loss + end_loss + pooled_loss) / 3






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     "google/bigbird-roberta-base"
 _lowerCamelCase        =     30_00
 _lowerCamelCase        =     1_05_00

 _lowerCamelCase        =     1_28
 _lowerCamelCase        =     3

 _lowerCamelCase        =     1
 _lowerCamelCase        =     5

 # tx_args
 _lowerCamelCase        =     3e-5
 _lowerCamelCase        =     0.0
 _lowerCamelCase        =     2_00_00
 _lowerCamelCase        =     0.0_0_9_5

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     os.makedirs(self.base_dir					,						exist_ok=__lowerCamelCase  )
     __A    :						Dict						 =     os.path.join(self.base_dir					,						self.save_dir  )
     __A    :						Dict						 =     self.batch_size_per_device * jax.device_count()






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     40_96  # no dynamic padding on TPUs

 def __call__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Optional[int]						 =     self.collate_fn(__lowerCamelCase  )
     __A    :						Tuple						 =     jax.tree_util.tree_map(__lowerCamelCase					,						__lowerCamelCase  )
     return batch

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A ,       __A    :						List[Any]						 =     self.fetch_inputs(features['''input_ids''']  )
     __A    :						Union[str, Any]						 =     {
         '''input_ids''': jnp.array(__lowerCamelCase					,						dtype=jnp.intaa  ),
         '''attention_mask''': jnp.array(__lowerCamelCase					,						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					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						Any						 =     [self._fetch_inputs(__lowerCamelCase  ) for ids in input_ids]
     return zip(*__lowerCamelCase  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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







def 		__lowercase     (   snake_case_  :  List[Any]				,snake_case_  :  Optional[Any]				,snake_case_  :  List[str]=None						)   ->Optional[int]:



    '''simple docstring'''







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







@partial(jax.pmap				,axis_name='''batch'''						)
def 		__lowercase     (   snake_case_  :  str				,snake_case_  :  Union[str, Any]				,**snake_case_  :  List[str]						)   ->Tuple:



    '''simple docstring'''







    def loss_fn(snake_case_  :  List[str]						):
        __A    :						str						 =     model_inputs.pop('''start_labels'''						)
        __A    :						str						 =     model_inputs.pop('''end_labels'''						)
        __A    :						int						 =     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    :						Union[str, Any]						 =     outputs

        return state.loss_fn(
            snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_				,)

    __A ,       __A    :						int						 =     jax.random.split(snake_case_						)
    __A    :						str						 =     jax.value_and_grad(snake_case_						)
    __A ,       __A    :						Optional[int]						 =     grad_fn(state.params						)
    __A    :						List[str]						 =     jax.lax.pmean({'''loss''': loss}				,axis_name='''batch'''						)
    __A    :						List[str]						 =     jax.lax.pmean(snake_case_				,'''batch'''						)

    __A    :						str						 =     state.apply_gradients(grads=snake_case_						)
    return state, metrics, new_drp_rng







@partial(jax.pmap				,axis_name='''batch'''						)
def 		__lowercase     (   snake_case_  :  int				,**snake_case_  :  Union[str, Any]						)   ->List[str]:



    '''simple docstring'''







    __A    :						Tuple						 =     model_inputs.pop('''start_labels'''						)
    __A    :						Dict						 =     model_inputs.pop('''end_labels'''						)
    __A    :						int						 =     model_inputs.pop('''pooled_labels'''						)

    __A    :						List[str]						 =     state.apply_fn(**snake_case_				,params=state.params				,train=snake_case_						)
    __A ,       __A ,       __A    :						Dict						 =     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 		__snake_case					(		train_state.TrainState     ):





 """simple docstring"""


 _lowerCamelCase        =     struct.field(pytree_node=SCREAMING_SNAKE_CASE__     )






@dataclass
class 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						Tuple						 =     model.params
     __A    :						Union[str, Any]						 =     TrainState.create(
         apply_fn=model.__call__					,						params=__lowerCamelCase					,						tx=__lowerCamelCase					,						loss_fn=__lowerCamelCase					,						)
     if ckpt_dir is not None:
         __A ,       __A ,       __A ,       __A ,       __A    :						Optional[Any]						 =     restore_checkpoint(__lowerCamelCase					,						__lowerCamelCase  )
         __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[str]						 =     build_tx(**__lowerCamelCase  )
         __A    :						int						 =     train_state.TrainState(
             step=__lowerCamelCase					,						apply_fn=model.__call__					,						params=__lowerCamelCase					,						tx=__lowerCamelCase					,						opt_state=__lowerCamelCase					,						)
         __A    :						int						 =     args
         __A    :						Optional[Any]						 =     data_collator
         __A    :						Tuple						 =     lr
         __A    :						List[Any]						 =     params
     __A    :						Dict						 =     jax_utils.replicate(__lowerCamelCase  )
     return state

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A    :						List[Any]						 =     self.args
     __A    :						Dict						 =     len(__lowerCamelCase  ) // args.batch_size

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

                 __A    :						Union[str, Any]						 =     self.evaluate(__lowerCamelCase					,						__lowerCamelCase  )
                 __A    :						Optional[Any]						 =     {
                     '''step''': state_step.item(),
                     '''eval_loss''': eval_loss.item(),
                     '''tr_loss''': tr_loss,
                     '''lr''': lr.item(),
                 }
                 tqdm.write(str(__lowerCamelCase  )  )
                 self.logger.log(__lowerCamelCase					,						commit=__lowerCamelCase  )

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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







def 		__lowercase     (   snake_case_  :  int				,snake_case_  :  Dict						)   ->Optional[int]:



    '''simple docstring'''







    print(F"""RESTORING CHECKPOINT FROM {save_dir}"""				,end=''' ... '''						)
    with open(os.path.join(snake_case_				,'''flax_model.msgpack'''						)				,'''rb'''						) as f:
        __A    :						List[Any]						 =     from_bytes(state.params				,f.read()						)

    with open(os.path.join(snake_case_				,'''opt_state.msgpack'''						)				,'''rb'''						) as f:
        __A    :						Optional[int]						 =     from_bytes(state.opt_state				,f.read()						)

    __A    :						Tuple						 =     joblib.load(os.path.join(snake_case_				,'''args.joblib'''						)						)
    __A    :						List[str]						 =     joblib.load(os.path.join(snake_case_				,'''data_collator.joblib'''						)						)

    with open(os.path.join(snake_case_				,'''training_state.json'''						)				,'''r'''						) as f:
        __A    :						Dict						 =     json.load(snake_case_						)
    __A    :						int						 =     training_state['''step''']

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







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  Any				,snake_case_  :  Dict				,snake_case_  :  str						)   ->List[str]:



    '''simple docstring'''







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







def 		__lowercase     (   snake_case_  :  List[str]				,snake_case_  :  List[Any]				,snake_case_  :  Union[str, Any]				,snake_case_  :  List[Any]				,snake_case_  :  str						)   ->List[str]:



    '''simple docstring'''







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

    __A    :						List[Any]						 =     scheduler_fn(snake_case_				,snake_case_				,snake_case_				,snake_case_						)

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




