





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor

__SCREAMING_SNAKE_CASE							:    List[str]          =random.Random()


def       UpperCamelCase__    (		lowerCAmelCase__       ,lowerCAmelCase__=1.0       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None ):
				if rng is None:
								lowercase							=     global_rng

				lowercase							=     []
				for batch_idx in range(shape[0] ):
								values.append([] )
								for _ in range(shape[1] ):
												values[-1].append(rng.random() * scale )

				return values

@require_torch
@require_torchaudio
class     A_		(			unittest.TestCase	):







			def __init__(				self  :							Optional[Any]   ,      snake_case__  :							str   ,      snake_case__  :							int=7   ,      snake_case__  :							Optional[Any]=4_00   ,      snake_case__  :							Tuple=20_00   ,      snake_case__  :							Any=24   ,      snake_case__  :							Optional[Any]=24   ,      snake_case__  :							List[str]=0.0   ,      snake_case__  :							Optional[int]=1_60_00   ,      snake_case__  :							Dict=True   ,      snake_case__  :							List[Any]=True   ,      ):
							lowercase							=     parent
							lowercase							=     batch_size
							lowercase							=     min_seq_length
							lowercase							=     max_seq_length
							lowercase							=     (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowercase							=     feature_size
							lowercase							=     num_mel_bins
							lowercase							=     padding_value
							lowercase							=     sampling_rate
							lowercase							=     return_attention_mask
							lowercase							=     do_normalize







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Tuple      ):
							return {
							    "feature_size": self.feature_size,
							    "num_mel_bins": self.num_mel_bins,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}







			def 						SCREAMING_SNAKE_CASE__	(				self  :							List[str]   ,      snake_case__  :							str=False   ,      snake_case__  :							Any=False      ):



							def _flatten(snake_case__  :							Tuple      ):
											return list(itertools.chain(*__A      )      )

							if equal_length:
											lowercase							=     [floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
							else:
											# make sure that inputs increase in size
											lowercase							=     [
											    floats_list((x, self.feature_size)      )
											    for x in range(self.min_seq_length   ,      self.max_seq_length   ,      self.seq_length_diff      )
											]
							if numpify:
											lowercase							=     [np.asarray(__A      ) for x in speech_inputs]
							return speech_inputs

@require_torch
@require_torchaudio
class     A_		(			UpperCamelCase__      ,      unittest.TestCase	):
			_A   :Tuple  				=      SpeechaTextFeatureExtractor if is_speech_available() else None







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Tuple      ):
							lowercase							=     SpeechaTextFeatureExtractionTester(self      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Dict   ,      snake_case__  :							Union[str, Any]      ):
							self.assertTrue(np.all(np.mean(__A   ,      axis=0      ) < 1E-3      )      )
							self.assertTrue(np.all(np.abs(np.var(__A   ,      axis=0      ) - 1      ) < 1E-3      )      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]      ):
							lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							# create three inputs of length 800, 1000, and 1200
							lowercase							=     [floats_list((1, x)      )[0] for x in range(8_00   ,      14_00   ,      2_00      )]
							lowercase							=     [np.asarray(__A      ) for speech_input in speech_inputs]

							# Test feature size
							lowercase							=     feature_extractor(__A   ,      padding=__A   ,      return_tensors="""np"""      ).input_features
							self.assertTrue(input_features.ndim == 3      )
							self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size      )

							# Test not batched input
							lowercase							=     feature_extractor(speech_inputs[0]   ,      return_tensors="""np"""      ).input_features
							lowercase							=     feature_extractor(np_speech_inputs[0]   ,      return_tensors="""np"""      ).input_features
							self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1E-3      )      )

							# Test batched
							lowercase							=     feature_extractor(__A   ,      return_tensors="""np"""      ).input_features
							lowercase							=     feature_extractor(__A   ,      return_tensors="""np"""      ).input_features
							for enc_seq_a, enc_seq_a in zip(__A   ,      __A      ):
											self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1E-3      )      )

							# Test 2-D numpy arrays are batched.
							lowercase							=     [floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
							lowercase							=     np.asarray(__A      )
							lowercase							=     feature_extractor(__A   ,      return_tensors="""np"""      ).input_features
							lowercase							=     feature_extractor(__A   ,      return_tensors="""np"""      ).input_features
							for enc_seq_a, enc_seq_a in zip(__A   ,      __A      ):
											self.assertTrue(np.allclose(__A   ,      __A   ,      atol=1E-3      )      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Dict      ):
							lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							lowercase							=     [floats_list((1, x)      )[0] for x in range(8_00   ,      14_00   ,      2_00      )]

							lowercase							=     ["""longest""", """max_length""", """do_not_pad"""]
							lowercase							=     [None, 16, None]
							for max_length, padding in zip(__A   ,      __A      ):
											lowercase							=     feature_extractor(
											    __A   ,      padding=__A   ,      max_length=__A   ,      return_attention_mask=__A      )
											lowercase							=     inputs.input_features
											lowercase							=     inputs.attention_mask
											lowercase							=     [np.sum(__A      ) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]      )
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]      )
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Tuple      ):
							lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							lowercase							=     [floats_list((1, x)      )[0] for x in range(8_00   ,      14_00   ,      2_00      )]

							lowercase							=     ["""longest""", """max_length""", """do_not_pad"""]
							lowercase							=     [None, 16, None]
							for max_length, padding in zip(__A   ,      __A      ):
											lowercase							=     feature_extractor(
											    __A   ,      max_length=__A   ,      padding=__A   ,      return_tensors="""np"""   ,      return_attention_mask=__A      )
											lowercase							=     inputs.input_features
											lowercase							=     inputs.attention_mask
											lowercase							=     [np.sum(__A      ) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]      )
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6      )
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]      )
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6      )
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Union[str, Any]      ):
							lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							lowercase							=     [floats_list((1, x)      )[0] for x in range(8_00   ,      14_00   ,      2_00      )]
							lowercase							=     feature_extractor(
							    __A   ,      padding="""max_length"""   ,      max_length=4   ,      truncation=__A   ,      return_tensors="""np"""   ,      return_attention_mask=__A   ,      )
							lowercase							=     inputs.input_features
							lowercase							=     inputs.attention_mask
							lowercase							=     np.sum(attention_mask == 1   ,      axis=1      )

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]      )
							self._check_zero_mean_unit_variance(input_features[1]      )
							self._check_zero_mean_unit_variance(input_features[2]      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Optional[Any]      ):
							lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							lowercase							=     [floats_list((1, x)      )[0] for x in range(8_00   ,      14_00   ,      2_00      )]
							lowercase							=     feature_extractor(
							    __A   ,      padding="""longest"""   ,      max_length=4   ,      truncation=__A   ,      return_tensors="""np"""   ,      return_attention_mask=__A   ,      )
							lowercase							=     inputs.input_features
							lowercase							=     inputs.attention_mask
							lowercase							=     np.sum(attention_mask == 1   ,      axis=1      )

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]      )
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]      )
							self._check_zero_mean_unit_variance(input_features[2]      )

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape   ,      (3, 4, 24)      )

							lowercase							=     [floats_list((1, x)      )[0] for x in range(8_00   ,      14_00   ,      2_00      )]
							lowercase							=     feature_extractor(
							    __A   ,      padding="""longest"""   ,      max_length=16   ,      truncation=__A   ,      return_tensors="""np"""   ,      return_attention_mask=__A   ,      )
							lowercase							=     inputs.input_features
							lowercase							=     inputs.attention_mask
							lowercase							=     np.sum(attention_mask == 1   ,      axis=1      )

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]      )
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]      )
							self._check_zero_mean_unit_variance(input_features[2]      )

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape   ,      (3, 6, 24)      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Any      ):
							import torch

							lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							lowercase							=     np.random.rand(1_00   ,      32      ).astype(np.floataa      )
							lowercase							=     np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											lowercase							=     feature_extractor.pad([{"""input_features""": inputs}]   ,      return_tensors="""np"""      )
											self.assertTrue(np_processed.input_features.dtype == np.floataa      )
											lowercase							=     feature_extractor.pad([{"""input_features""": inputs}]   ,      return_tensors="""pt"""      )
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa      )







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Union[str, Any]   ,      snake_case__  :							Union[str, Any]      ):
							from datasets import load_dataset

							lowercase							=     load_dataset("""hf-internal-testing/librispeech_asr_dummy"""   ,      """clean"""   ,      split="""validation"""      )
							# automatic decoding with librispeech
							lowercase							=     ds.sort("""id"""      ).select(range(__A      )      )[:num_samples]["""audio"""]

							return [x["array"] for x in speech_samples]







			def 						SCREAMING_SNAKE_CASE__	(				self  :							Dict      ):
							lowercase							=     np.array([
							    -1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
							    -1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
							    -1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
							]      )
							# fmt: on

							lowercase							=     self._load_datasamples(1      )
							lowercase							=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							lowercase							=     feature_extractor(__A   ,      return_tensors="""pt"""      ).input_features
							self.assertEquals(input_features.shape   ,      (1, 5_84, 24)      )
							self.assertTrue(np.allclose(input_features[0, 0, :30]   ,      __A   ,      atol=1E-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 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       jnp.floataa
							lowerCamelCase_							     =       True


							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									super().setup()
									SCREAMING_SNAKE_CASE__								=   nn.Dense(5				,					dtype=self.dtype		)


							def __call__(				self     :List[Any]				,					*__A     :int				,					**__A     :Optional[Any]		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   super().__call__(*__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   self.cls(outputs[2]		)
									return outputs[:2] + (cls_out,)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       FlaxBigBirdForNaturalQuestionsModule






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

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




@dataclass
class 	UpperCamelCase_							:
							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_							     =       0.0095

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


							def   _snake_case	(				self     :str		) -> Optional[int]:



									"""simple docstring"""



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




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       40_96  # no dynamic padding on TPUs


							def __call__(				self     :Optional[Any]				,					__A     :Optional[int]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.collate_fn(__A		)
									SCREAMING_SNAKE_CASE__								=   jax.tree_util.tree_map(__A				,					__A		)
									return batch


							def   _snake_case	(				self     :List[Any]				,					__A     :Union[str, Any]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.fetch_inputs(features["""input_ids"""]		)
									SCREAMING_SNAKE_CASE__								=   {
									    """input_ids""": jnp.array(__A				,					dtype=jnp.intaa		),
									    """attention_mask""": jnp.array(__A				,					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   _snake_case	(				self     :Tuple				,					__A     :list		) -> Dict:



									"""simple docstring"""



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


							def   _snake_case	(				self     :List[str]				,					__A     :list		) -> Union[str, Any]:



									"""simple docstring"""



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






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






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

				SCREAMING_SNAKE_CASE__								=   state.apply_fn(**UpperCamelCase__       , params=UpperCamelCase__       , dropout_rng=UpperCamelCase__       , train=UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   outputs

				return state.loss_fn(
				    UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , )

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

		SCREAMING_SNAKE_CASE__								=   state.apply_gradients(grads=UpperCamelCase__     )
		return state, metrics, new_drp_rng






@partial(jax.pmap       , axis_name="""batch"""     )
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]       , **UpperCamelCase__:				Dict     ):
		SCREAMING_SNAKE_CASE__								=   model_inputs.pop("""start_labels"""     )
		SCREAMING_SNAKE_CASE__								=   model_inputs.pop("""end_labels"""     )
		SCREAMING_SNAKE_CASE__								=   model_inputs.pop("""pooled_labels"""     )

		SCREAMING_SNAKE_CASE__								=   state.apply_fn(**UpperCamelCase__       , params=state.params       , train=UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   outputs

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




class 	UpperCamelCase_							(  train_state.TrainState     ):
							lowerCamelCase_							     =       struct.field(pytree_node=UpperCamelCase__     )




@dataclass
class 	UpperCamelCase_							:
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       42
							lowerCamelCase_							     =       None


							def   _snake_case	(				self     :List[Any]				,					__A     :str				,					__A     :str				,					__A     :str				,					__A     :Tuple=None		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   model.params
									SCREAMING_SNAKE_CASE__								=   TrainState.create(
									    apply_fn=model.__call__				,					params=__A				,					tx=__A				,					loss_fn=__A				,					)
									if ckpt_dir is not None:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   restore_checkpoint(__A				,					__A		)
											SCREAMING_SNAKE_CASE__								=   {
											    """lr""": args.lr,
											    """init_lr""": args.init_lr,
											    """warmup_steps""": args.warmup_steps,
											    """num_train_steps""": num_train_steps,
											    """weight_decay""": args.weight_decay,
											}
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   build_tx(**__A		)
											SCREAMING_SNAKE_CASE__								=   train_state.TrainState(
											    step=__A				,					apply_fn=model.__call__				,					params=__A				,					tx=__A				,					opt_state=__A				,					)
											SCREAMING_SNAKE_CASE__								=   args
											SCREAMING_SNAKE_CASE__								=   data_collator
											SCREAMING_SNAKE_CASE__								=   lr
											SCREAMING_SNAKE_CASE__								=   params
									SCREAMING_SNAKE_CASE__								=   jax_utils.replicate(__A		)
									return state


							def   _snake_case	(				self     :Optional[Any]				,					__A     :Optional[int]				,					__A     :int				,					__A     :int		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.args
									SCREAMING_SNAKE_CASE__								=   len(__A		) // args.batch_size

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

															SCREAMING_SNAKE_CASE__								=   self.evaluate(__A				,					__A		)
															SCREAMING_SNAKE_CASE__								=   {
															    """step""": state_step.item(),
															    """eval_loss""": eval_loss.item(),
															    """tr_loss""": tr_loss,
															    """lr""": lr.item(),
															}
															tqdm.write(str(__A		)		)
															self.logger.log(__A				,					commit=__A		)

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


							def   _snake_case	(				self     :List[str]				,					__A     :Dict				,					__A     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   get_batched_dataset(__A				,					self.args.batch_size		)
									SCREAMING_SNAKE_CASE__								=   len(__A		) // self.args.batch_size
									SCREAMING_SNAKE_CASE__								=   jnp.array(0				,					dtype=jnp.floataa		)
									SCREAMING_SNAKE_CASE__								=   0
									for batch in tqdm(__A				,					total=__A				,					desc="""Evaluating ... """		):
											SCREAMING_SNAKE_CASE__								=   self.data_collator(__A		)
											SCREAMING_SNAKE_CASE__								=   self.val_step_fn(__A				,					**__A		)
											running_loss += jax_utils.unreplicate(metrics["""loss"""]		)
											i += 1
									return running_loss / i


							def   _snake_case	(				self     :List[Any]				,					__A     :Any				,					__A     :Dict		) -> Union[str, Any]:



									"""simple docstring"""



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






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

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

		SCREAMING_SNAKE_CASE__								=   joblib.load(os.path.join(UpperCamelCase__       , """args.joblib"""     )     )
		SCREAMING_SNAKE_CASE__								=   joblib.load(os.path.join(UpperCamelCase__       , """data_collator.joblib"""     )     )

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

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






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






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

		SCREAMING_SNAKE_CASE__								=   scheduler_fn(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   optax.adamw(learning_rate=UpperCamelCase__       , weight_decay=UpperCamelCase__       , mask=UpperCamelCase__     )
		return tx, lr