

import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        DebertaVaForMaskedLM,
        DebertaVaForMultipleChoice,
        DebertaVaForQuestionAnswering,
        DebertaVaForSequenceClassification,
        DebertaVaForTokenClassification,
        DebertaVaModel,
    )
    from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   def __init__(       self					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			List[str]=13     , __lowerCamelCase					:			Optional[int]=7     , __lowerCamelCase					:			str=True     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			List[str]=True     , __lowerCamelCase					:			List[str]=True     , __lowerCamelCase					:			Tuple=99     , __lowerCamelCase					:			Any=32     , __lowerCamelCase					:			Dict=5     , __lowerCamelCase					:			Any=4     , __lowerCamelCase					:			Tuple=37     , __lowerCamelCase					:			Dict="gelu"     , __lowerCamelCase					:			Optional[int]=0.1     , __lowerCamelCase					:			Dict=0.1     , __lowerCamelCase					:			List[str]=512     , __lowerCamelCase					:			str=16     , __lowerCamelCase					:			Union[str, Any]=2     , __lowerCamelCase					:			Any=0.02     , __lowerCamelCase					:			Tuple=False     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			int="None"     , __lowerCamelCase					:			Any=3     , __lowerCamelCase					:			List[str]=4     , __lowerCamelCase					:			List[str]=None     , ):
       SCREAMING_SNAKE_CASE										=   parent
       SCREAMING_SNAKE_CASE										=   batch_size
       SCREAMING_SNAKE_CASE										=   seq_length
       SCREAMING_SNAKE_CASE										=   is_training
       SCREAMING_SNAKE_CASE										=   use_input_mask
       SCREAMING_SNAKE_CASE										=   use_token_type_ids
       SCREAMING_SNAKE_CASE										=   use_labels
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   hidden_size
       SCREAMING_SNAKE_CASE										=   num_hidden_layers
       SCREAMING_SNAKE_CASE										=   num_attention_heads
       SCREAMING_SNAKE_CASE										=   intermediate_size
       SCREAMING_SNAKE_CASE										=   hidden_act
       SCREAMING_SNAKE_CASE										=   hidden_dropout_prob
       SCREAMING_SNAKE_CASE										=   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE										=   max_position_embeddings
       SCREAMING_SNAKE_CASE										=   type_vocab_size
       SCREAMING_SNAKE_CASE										=   type_sequence_label_size
       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   num_labels
       SCREAMING_SNAKE_CASE										=   num_choices
       SCREAMING_SNAKE_CASE										=   relative_attention
       SCREAMING_SNAKE_CASE										=   position_biased_input
       SCREAMING_SNAKE_CASE										=   pos_att_type
       SCREAMING_SNAKE_CASE										=   scope





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size			)

       SCREAMING_SNAKE_CASE										=   None
       if self.use_input_mask:
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size, self.seq_length]     , vocab_size=2			)

       SCREAMING_SNAKE_CASE										=   None
       if self.use_token_type_ids:
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size, self.seq_length]     , self.type_vocab_size			)

       SCREAMING_SNAKE_CASE										=   None
       SCREAMING_SNAKE_CASE										=   None
       SCREAMING_SNAKE_CASE										=   None
       if self.use_labels:
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size]     , self.type_sequence_label_size			)
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size, self.seq_length]     , self.num_labels			)
           SCREAMING_SNAKE_CASE										=   ids_tensor([self.batch_size]     , self.num_choices			)

       SCREAMING_SNAKE_CASE										=   self.get_config()

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





   def      _snake_case		(       self					:			int			):
       return 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     , initializer_range=self.initializer_range     , relative_attention=self.relative_attention     , position_biased_input=self.position_biased_input     , pos_att_type=self.pos_att_type     , )





   def      _snake_case		(       self					:			Any     , __lowerCamelCase					:			List[str]			):
       self.parent.assertListEqual(list(result.loss.size()			)     , []			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			str     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Any			):
       SCREAMING_SNAKE_CASE										=   DebertaVaModel(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase			)[0]
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , token_type_ids=__lowerCamelCase			)[0]
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase			)[0]

       self.parent.assertListEqual(list(sequence_output.size()			)     , [self.batch_size, self.seq_length, self.hidden_size]			)





   def      _snake_case		(       self					:			List[Any]     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   DebertaVaForMaskedLM(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase			)

       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)			)





   def      _snake_case		(       self					:			Dict     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			int     , __lowerCamelCase					:			int     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.num_labels
       SCREAMING_SNAKE_CASE										=   DebertaVaForSequenceClassification(__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase			)
       self.parent.assertListEqual(list(result.logits.size()			)     , [self.batch_size, self.num_labels]			)
       self.check_loss_output(__lowerCamelCase			)





   def      _snake_case		(       self					:			int     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Any     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.num_labels
       SCREAMING_SNAKE_CASE										=   DebertaVaForTokenClassification(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)			)





   def      _snake_case		(       self					:			Optional[int]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Tuple     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			List[Any]     , __lowerCamelCase					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   DebertaVaForQuestionAnswering(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   model(
           __lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , start_positions=__lowerCamelCase     , end_positions=__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      _snake_case		(       self					:			Optional[Any]     , __lowerCamelCase					:			Optional[int]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			str     , __lowerCamelCase					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			str			):
       SCREAMING_SNAKE_CASE										=   DebertaVaForMultipleChoice(config=__lowerCamelCase			)
       model.to(__lowerCamelCase			)
       model.eval()
       SCREAMING_SNAKE_CASE										=   input_ids.unsqueeze(1			).expand(-1     , self.num_choices     , -1			).contiguous()
       SCREAMING_SNAKE_CASE										=   token_type_ids.unsqueeze(1			).expand(-1     , self.num_choices     , -1			).contiguous()
       SCREAMING_SNAKE_CASE										=   input_mask.unsqueeze(1			).expand(-1     , self.num_choices     , -1			).contiguous()
       SCREAMING_SNAKE_CASE										=   model(
           __lowerCamelCase     , attention_mask=__lowerCamelCase     , token_type_ids=__lowerCamelCase     , labels=__lowerCamelCase     , )
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_choices)			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       SCREAMING_SNAKE_CASE										=   self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE
       )							,	(
           SCREAMING_SNAKE_CASE
       )							,	(
           SCREAMING_SNAKE_CASE
       )							,	(
           SCREAMING_SNAKE_CASE
       )							,	(
           SCREAMING_SNAKE_CASE
       )							,	(
           SCREAMING_SNAKE_CASE
       )							,	(
           SCREAMING_SNAKE_CASE
       )							,	
       )										=   config_and_inputs
       SCREAMING_SNAKE_CASE										=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict





@require_torch
class 			_SCREAMING_SNAKE_CASE (   __snake_case					,       __snake_case					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      (
       (
           DebertaVaModel,
           DebertaVaForMaskedLM,
           DebertaVaForSequenceClassification,
           DebertaVaForTokenClassification,
           DebertaVaForQuestionAnswering,
           DebertaVaForMultipleChoice,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase__				=      (
       {
           "feature-extraction": DebertaVaModel,
           "fill-mask": DebertaVaForMaskedLM,
           "question-answering": DebertaVaForQuestionAnswering,
           "text-classification": DebertaVaForSequenceClassification,
           "token-classification": DebertaVaForTokenClassification,
           "zero-shot": DebertaVaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )

   lowerCamelCase__				=      True
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False
   lowerCamelCase__				=      False





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   DebertaVaModelTester(self			)
       SCREAMING_SNAKE_CASE										=   ConfigTester(self     , config_class=__lowerCamelCase     , hidden_size=37			)





   def      _snake_case		(       self					:			Union[str, Any]			):
       self.config_tester.run_common_tests()





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_model(*__lowerCamelCase			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_sequence_classification(*__lowerCamelCase			)





   def      _snake_case		(       self					:			str			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_masked_lm(*__lowerCamelCase			)





   def      _snake_case		(       self					:			int			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_question_answering(*__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_token_classification(*__lowerCamelCase			)





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_deberta_for_multiple_choice(*__lowerCamelCase			)





   @slow
   def      _snake_case		(       self					:			Optional[int]			):
       for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           SCREAMING_SNAKE_CASE										=   DebertaVaModel.from_pretrained(__lowerCamelCase			)
           self.assertIsNotNone(__lowerCamelCase			)





@require_torch
@require_sentencepiece
@require_tokenizers
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   @unittest.skip(reason="Model not available yet"			)
   def      _snake_case		(       self					:			List[Any]			):
       pass





   @slow
   def      _snake_case		(       self					:			Tuple			):
       SCREAMING_SNAKE_CASE										=   DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge"			)

       SCREAMING_SNAKE_CASE										=   torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]			)
       SCREAMING_SNAKE_CASE										=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
       with torch.no_grad():
           SCREAMING_SNAKE_CASE										=   model(__lowerCamelCase     , attention_mask=__lowerCamelCase			)[0]
       # compare the actual values for a slice.
       SCREAMING_SNAKE_CASE										=   torch.tensor(
           [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]			)
       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]     , __lowerCamelCase     , atol=1e-4			)     , f"{output[:, 1:4, 1:4]}"			)

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 (						UpperCAmelCase_				):
 __lowerCAmelCase	:  BigBirdConfig
 __lowerCAmelCase	:  jnp.dtype	   =      jnp.floataa
 __lowerCAmelCase	:  bool	   =      True


 def   lowercase__  (       self):


   '''simple docstring'''




   super().setup()
   lowercase__	: Dict     		=   nn.Dense(5       ,	dtype=self.dtype)



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


   '''simple docstring'''




   lowercase__	: List[str]     		=   super().__call__(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)
   lowercase__	: List[str]     		=   self.cls(outputs[2])
   return outputs[:2] + (cls_out,)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Optional[int]	   =      FlaxBigBirdForNaturalQuestionsModule

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






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

  lowercase__	: int     		=   partial(lowercase_			,			reduction=jnp.mean )
  lowercase__	: Tuple     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: List[Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  lowercase__	: Union[str, Any]     		=   cross_entropy(lowercase_			,			lowercase_ )
  return (start_loss + end_loss + pooled_loss) / 3



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  str	   =      "google/bigbird-roberta-base"
 __lowerCAmelCase	:  int	   =      3_000
 __lowerCAmelCase	:  int	   =      10_500

 __lowerCAmelCase	:  int	   =      128
 __lowerCAmelCase	:  int	   =      3

 __lowerCAmelCase	:  int	   =      1
 __lowerCAmelCase	:  int	   =      5

 # tx_args
 __lowerCAmelCase	:  float	   =      3e-5
 __lowerCAmelCase	:  float	   =      0.0
 __lowerCAmelCase	:  int	   =      20_000
 __lowerCAmelCase	:  float	   =      0.0_095

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


 def   lowercase__  (       self):


   '''simple docstring'''




   os.makedirs(self.base_dir       ,	exist_ok=SCREAMING_SNAKE_CASE_)
   lowercase__	: Any     		=   os.path.join(self.base_dir       ,	self.save_dir)
   lowercase__	: str     		=   self.batch_size_per_device * jax.device_count()



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  int
 __lowerCAmelCase	:  int	   =      4_096  # no dynamic padding on TPUs


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.collate_fn(SCREAMING_SNAKE_CASE_)
   lowercase__	: List[Any]     		=   jax.tree_util.tree_map(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   return batch


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__		,     lowercase__	: str     		=   self.fetch_inputs(features["""input_ids"""])
   lowercase__	: str     		=   {
       """input_ids""": jnp.array(SCREAMING_SNAKE_CASE_       ,	dtype=jnp.intaa),
       """attention_mask""": jnp.array(SCREAMING_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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   [self._fetch_inputs(SCREAMING_SNAKE_CASE_) for ids in input_ids]
   return zip(*SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None )		->						Optional[Any]:


  '''simple docstring'''






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

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			**lowercase_ )		->						int:


  '''simple docstring'''






  def loss_fn(lowercase_ ):
    lowercase__	: Dict     		=   model_inputs.pop("""start_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""end_labels""" )
    lowercase__	: List[Any]     		=   model_inputs.pop("""pooled_labels""" )

    lowercase__	: List[Any]     		=   state.apply_fn(**lowercase_			,			params=lowercase_			,			dropout_rng=lowercase_			,			train=lowercase_ )
    lowercase__		,     lowercase__		,     lowercase__	: Any     		=   outputs

    return state.loss_fn(
        lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)

  lowercase__		,     lowercase__	: Optional[int]     		=   jax.random.split(lowercase_ )
  lowercase__	: Tuple     		=   jax.value_and_grad(lowercase_ )
  lowercase__		,     lowercase__	: Optional[int]     		=   grad_fn(state.params )
  lowercase__	: Tuple     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  lowercase__	: Any     		=   jax.lax.pmean(lowercase_			,			"""batch""" )

  lowercase__	: str     		=   state.apply_gradients(grads=lowercase_ )
  return state, metrics, new_drp_rng

@partial(jax.pmap			,			axis_name="""batch""" )
def 		UpperCamelCase						(   lowercase_			,			**lowercase_ )		->						str:


  '''simple docstring'''






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

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

  lowercase__	: Optional[Any]     		=   state.loss_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
  lowercase__	: List[str]     		=   jax.lax.pmean({"""loss""": loss}			,			axis_name="""batch""" )
  return metrics



class    _snake_case (						train_state.TrainState				):
 __lowerCAmelCase	:  Callable	   =      struct.field(pytree_node=UpperCAmelCase_				)



@dataclass
class    _snake_case :
 __lowerCAmelCase	:  Args
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  Callable
 __lowerCAmelCase	:  wandb
 __lowerCAmelCase	:  Callable	   =      None


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=None):


   '''simple docstring'''




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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.args
   lowercase__	: List[str]     		=   len(SCREAMING_SNAKE_CASE_) // args.batch_size

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

         lowercase__	: Tuple     		=   self.evaluate(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
         lowercase__	: List[Any]     		=   {
             """step""": state_step.item(),
             """eval_loss""": eval_loss.item(),
             """tr_loss""": tr_loss,
             """lr""": lr.item(),
         }
         tqdm.write(str(SCREAMING_SNAKE_CASE_))
         self.logger.log(SCREAMING_SNAKE_CASE_       ,	commit=SCREAMING_SNAKE_CASE_)

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


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




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

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






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

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

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

  with open(os.path.join(lowercase_			,			"""training_state.json""" )			,			"""r""" ) as f:
    lowercase__	: int     		=   json.load(lowercase_ )
  lowercase__	: Optional[Any]     		=   training_state["""step"""]

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   num_train_steps - warmup_steps
  lowercase__	: int     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=lowercase_			,			transition_steps=lowercase_ )
  lowercase__	: Optional[int]     		=   optax.linear_schedule(init_value=lowercase_			,			end_value=1E-7			,			transition_steps=lowercase_ )
  lowercase__	: Any     		=   optax.join_schedules(schedules=[warmup_fn, decay_fn]			,			boundaries=[warmup_steps] )
  return lr



def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						Optional[int]:


  '''simple docstring'''






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

  lowercase__	: Optional[int]     		=   scheduler_fn(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )

  lowercase__	: int     		=   optax.adamw(learning_rate=lowercase_			,			weight_decay=lowercase_			,			mask=lowercase_ )
  return tx, lr






