




'''simple docstring'''


import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_snake_case				:				Union[str, Any]       							=		'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
_snake_case				:				Optional[int]       							=		direct_transformers_import(PATH_TO_TRANSFORMERS)

_snake_case				:				str       							=		transformers.models.auto.configuration_auto.CONFIG_MAPPING

_snake_case				:				Tuple       							=		{
    # used to compute the property `self.chunk_length`
    'EncodecConfig': ['overlap'],
    # used as `self.bert_model = BertModel(config, ...)`
    'DPRConfig': True,
    # not used in modeling files, but it's an important information
    'FSMTConfig': ['langs'],
    # used internally in the configuration class file
    'GPTNeoConfig': ['attention_types'],
    # used internally in the configuration class file
    'EsmConfig': ['is_folding_model'],
    # used during training (despite we don't have training script for these models yet)
    'Mask2FormerConfig': ['ignore_value'],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    'OneFormerConfig': ['ignore_value', 'norm'],
    # used during preprocessing and collation, see `collating_graphormer.py`
    'GraphormerConfig': ['spatial_pos_max'],
    # used internally in the configuration class file
    'T5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    'MT5Config': ['feed_forward_proj', 'tokenizer_class'],
    'UMT5Config': ['feed_forward_proj', 'tokenizer_class'],
    # used internally in the configuration class file
    'LongT5Config': ['feed_forward_proj'],
    # used internally in the configuration class file
    'SwitchTransformersConfig': ['feed_forward_proj'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'BioGptConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'GLPNConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'SegformerConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'CvtConfig': ['layer_norm_eps'],
    # having default values other than `1e-5` - we can't fix them without breaking
    'PerceiverConfig': ['layer_norm_eps'],
    # used internally to calculate the feature size
    'InformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'TimeSeriesTransformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate the feature size
    'AutoformerConfig': ['num_static_real_features', 'num_time_features'],
    # used internally to calculate `mlp_dim`
    'SamVisionConfig': ['mlp_ratio'],
    # For (head) training, but so far not implemented
    'ClapAudioConfig': ['num_classes'],
    # Not used, but providing useful information to users
    'SpeechT5HifiGanConfig': ['sampling_rate'],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        'CLIPSegConfig': True,
        'DeformableDetrConfig': True,
        'DetaConfig': True,
        'DinatConfig': True,
        'DonutSwinConfig': True,
        'EfficientFormerConfig': True,
        'FSMTConfig': True,
        'JukeboxConfig': True,
        'LayoutLMv2Config': True,
        'MaskFormerSwinConfig': True,
        'MT5Config': True,
        'NatConfig': True,
        'OneFormerConfig': True,
        'PerceiverConfig': True,
        'RagConfig': True,
        'SpeechT5Config': True,
        'SwinConfig': True,
        'Swin2SRConfig': True,
        'Swinv2Config': True,
        'SwitchTransformersConfig': True,
        'TableTransformerConfig': True,
        'TapasConfig': True,
        'TransfoXLConfig': True,
        'UniSpeechConfig': True,
        'UniSpeechSatConfig': True,
        'WavLMConfig': True,
        'WhisperConfig': True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        'JukeboxPriorConfig': True,
        # TODO: @Younes (for `is_decoder`)
        'Pix2StructTextConfig': True,
    }
)
def 		snake_case_   (UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  Tuple			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							_a     					=    False
							for attribute in attributes:
														for modeling_source in source_strings:
																					# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
																					if (
																					    f'config.{attribute}' in modeling_source
																					    or f'getattr(config, "{attribute}"' in modeling_source
																					    or f'getattr(self.config, "{attribute}"' in modeling_source
																					):
																												_a     					=    True
																					# Deal with multi-line cases
																					elif (
																					    re.search(
																					        Rf'getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'			,						UpperCamelCase			,						)
																					    is not None
																					):
																												_a     					=    True
																					# `SequenceSummary` is called with `SequenceSummary(config)`
																					elif attribute in [
																					    "summary_type",
																					    "summary_use_proj",
																					    "summary_activation",
																					    "summary_last_dropout",
																					    "summary_proj_to_labels",
																					    "summary_first_dropout",
																					]:
																												if "SequenceSummary" in modeling_source:
																																			_a     					=    True
																					if attribute_used:
																												break
														if attribute_used:
																					break

    # common and important attributes, even if they do not always appear in the modeling files
							_a     					=    [
							    '''bos_index''',
							    '''eos_index''',
							    '''pad_index''',
							    '''unk_index''',
							    '''mask_index''',
							    '''image_size''',
							    '''use_cache''',
							    '''out_features''',
							    '''out_indices''',
							]
							_a     					=    ['''encoder_no_repeat_ngram_size''']

							# Special cases to be allowed
							_a     					=    True
							if not attribute_used:
														_a     					=    False
														for attribute in attributes:
																					# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
																					if attribute in ["is_encoder_decoder"] and default_value is True:
																												_a     					=    True
																					elif attribute in ["tie_word_embeddings"] and default_value is False:
																												_a     					=    True

																					# Allow cases without checking the default value in the configuration class
																					elif attribute in attributes_to_allow + attributes_used_in_generation:
																												_a     					=    True
																					elif attribute.endswith('''_token_id'''				):
																												_a     					=    True

																					# configuration class specific cases
																					if not case_allowed:
																												_a     					=    SPECIAL_CASES_TO_ALLOW.get(config_class.__name__			,						[]				)
																												_a     					=    allowed_cases is True or attribute in allowed_cases

							return attribute_used or case_allowed
def 		snake_case_   (UpperCamelCase	:  Dict				):





							'''simple docstring'''


							_a     					=    dict(inspect.signature(config_class.__init__				).parameters				)
							_a     					=    [x for x in list(signature.keys()				) if x not in ['''self''', '''kwargs''']]
							_a     					=    [signature[param].default for param in parameter_names]

							# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
							# as one variant is used, the test should pass
							_a     					=    {}
							if len(config_class.attribute_map				) > 0:
														_a     					=    {v: k for k, v in config_class.attribute_map.items()}

							# Get the path to modeling source files
							_a     					=    inspect.getsourcefile(UpperCamelCase				)
							_a     					=    os.path.dirname(UpperCamelCase				)
							# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
							_a     					=    [os.path.join(UpperCamelCase			,						UpperCamelCase				) for fn in os.listdir(UpperCamelCase				) if fn.startswith('''modeling_'''				)]

							# Get the source code strings
							_a     					=    []
							for path in modeling_paths:
														if os.path.isfile(UpperCamelCase				):
																					with open(UpperCamelCase				) as fp:
																												modeling_sources.append(fp.read()				)

							_a     					=    []
							for config_param, default_value in zip(UpperCamelCase			,						UpperCamelCase				):
														# `attributes` here is all the variant names for `config_param`
														_a     					=    [config_param]
														# some configuration classes have non-empty `attribute_map`, and both names could be used in the
														# corresponding modeling files. As long as one of them appears, it is fine.
														if config_param in reversed_attribute_map:
																					attributes.append(reversed_attribute_map[config_param]				)

														if not check_attribute_being_used(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				):
																					unused_attributes.append(attributes[0]				)

							return sorted(UpperCamelCase				)
def 		snake_case_   ():





							'''simple docstring'''


							_a     					=    {}
							for _config_class in list(CONFIG_MAPPING.values()				):
														# Skip deprecated models
														if "models.deprecated" in _config_class.__module__:
																					continue
														# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
														_a     					=    [
														    cls
														    for name, cls in inspect.getmembers(
														        inspect.getmodule(_config_class				)			,						lambda UpperCamelCase				: inspect.isclass(UpperCamelCase				)
														        and issubclass(UpperCamelCase			,						UpperCamelCase				)
														        and inspect.getmodule(UpperCamelCase				) == inspect.getmodule(_config_class				)			,						)
														]
														for config_class in config_classes_in_module:
																					_a     					=    check_config_attributes_being_used(UpperCamelCase				)
																					if len(UpperCamelCase				) > 0:
																												_a     					=    unused_attributes

							if len(UpperCamelCase				) > 0:
														_a     					=    '''The following configuration classes contain unused attributes in the corresponding modeling files:\n'''
														for name, attributes in configs_with_unused_attributes.items():
																					error += f'{name}: {attributes}\n'

														raise ValueError(UpperCamelCase				)


if __name__ == "__main__":
				check_config_attributes()







'''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       A   (		_a   ):
							lowercase_          =    42
							lowercase_          =    jnp.floataa
							lowercase_          =    True







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Dict:


														"""simple docstring"""


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







							def __call__(  self	:  List[Any]							,      *lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Dict )      ->							List[Any]:


														"""simple docstring"""


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



class       A   (		_a   ):
							lowercase_          =    FlaxBigBirdForNaturalQuestionsModule



def 		snake_case_   (UpperCamelCase	:  Any			,						UpperCamelCase	:  str			,						UpperCamelCase	:  int			,						UpperCamelCase	:  Tuple			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  int				):





							'''simple docstring'''


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

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

@dataclass
class       A   :
							lowercase_          =    "google/bigbird-roberta-base"
							lowercase_          =    3000
							lowercase_          =    1_0500

							lowercase_          =    128
							lowercase_          =    3

							lowercase_          =    1
							lowercase_          =    5

							# tx_args
							lowercase_          =    3e-5
							lowercase_          =    0.0
							lowercase_          =    2_0000
							lowercase_          =    0.0095

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







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Any:


														"""simple docstring"""


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



@dataclass
class       A   :
							lowercase_          =    42
							lowercase_          =    4096  # no dynamic padding on TPUs







							def __call__(  self	:  str							,      lowerCAmelCase_	:  Union[str, Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    self.collate_fn(lowerCAmelCase_ )
														_a     					=    jax.tree_util.tree_map(lowerCAmelCase_							,      lowerCAmelCase_ )
														return batch







							def        __lowerCAmelCase      (  self	:  List[Any]							,      lowerCAmelCase_	:  List[Any] )      ->							int:


														"""simple docstring"""


														_a					,							_a     					=    self.fetch_inputs(features['''input_ids'''] )
														_a     					=    {
														    '''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        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  list )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    [self._fetch_inputs(lowerCAmelCase_ ) for ids in input_ids]
														return zip(*lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  list )      ->							str:


														"""simple docstring"""


														_a     					=    [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 		snake_case_   (UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  List[Any]			,						UpperCamelCase	:  Dict=None				):





							'''simple docstring'''


							if seed is not None:
														_a     					=    dataset.shuffle(seed=UpperCamelCase				)
							for i in range(len(UpperCamelCase				) // batch_size				):
														_a     					=    dataset[i * batch_size : (i + 1) * batch_size]
														yield dict(UpperCamelCase				)
@partial(jax.pmap			,						axis_name='''batch'''				)
def 		snake_case_   (UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Tuple			,						**UpperCamelCase	:  str				):





							'''simple docstring'''


							def loss_fn(UpperCamelCase	:  List[str]				):
														_a     					=    model_inputs.pop('''start_labels'''				)
														_a     					=    model_inputs.pop('''end_labels'''				)
														_a     					=    model_inputs.pop('''pooled_labels'''				)

														_a     					=    state.apply_fn(**UpperCamelCase			,						params=UpperCamelCase			,						dropout_rng=UpperCamelCase			,						train=UpperCamelCase				)
														_a					,							_a					,							_a     					=    outputs

														return state.loss_fn(
														    UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						)

							_a					,							_a     					=    jax.random.split(UpperCamelCase				)
							_a     					=    jax.value_and_grad(UpperCamelCase				)
							_a					,							_a     					=    grad_fn(state.params				)
							_a     					=    jax.lax.pmean({'''loss''': loss}			,						axis_name='''batch'''				)
							_a     					=    jax.lax.pmean(UpperCamelCase			,						'''batch'''				)

							_a     					=    state.apply_gradients(grads=UpperCamelCase				)
							return state, metrics, new_drp_rng
@partial(jax.pmap			,						axis_name='''batch'''				)
def 		snake_case_   (UpperCamelCase	:  Any			,						**UpperCamelCase	:  Optional[int]				):





							'''simple docstring'''


							_a     					=    model_inputs.pop('''start_labels'''				)
							_a     					=    model_inputs.pop('''end_labels'''				)
							_a     					=    model_inputs.pop('''pooled_labels'''				)

							_a     					=    state.apply_fn(**UpperCamelCase			,						params=state.params			,						train=UpperCamelCase				)
							_a					,							_a					,							_a     					=    outputs

							_a     					=    state.loss_fn(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				)
							_a     					=    jax.lax.pmean({'''loss''': loss}			,						axis_name='''batch'''				)
							return metrics

class       A   (		train_state.TrainState   ):
							lowercase_          =    struct.field(pytree_node=_a   )



@dataclass
class       A   :
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    42
							lowercase_          =    None







							def        __lowerCAmelCase      (  self	:  Tuple							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Optional[int]=None )      ->							List[str]:


														"""simple docstring"""


														_a     					=    model.params
														_a     					=    TrainState.create(
														    apply_fn=model.__call__							,      params=lowerCAmelCase_							,      tx=lowerCAmelCase_							,      loss_fn=lowerCAmelCase_							,      )
														if ckpt_dir is not None:
																					_a					,							_a					,							_a					,							_a					,							_a     					=    restore_checkpoint(lowerCAmelCase_							,      lowerCAmelCase_ )
																					_a     					=    {
																					    '''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     					=    build_tx(**lowerCAmelCase_ )
																					_a     					=    train_state.TrainState(
																					    step=lowerCAmelCase_							,      apply_fn=model.__call__							,      params=lowerCAmelCase_							,      tx=lowerCAmelCase_							,      opt_state=lowerCAmelCase_							,      )
																					_a     					=    args
																					_a     					=    data_collator
																					_a     					=    lr
																					_a     					=    params
														_a     					=    jax_utils.replicate(lowerCAmelCase_ )
														return state







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Any							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  Optional[Any] )      ->							int:


														"""simple docstring"""


														_a     					=    self.args
														_a     					=    len(lowerCAmelCase_ ) // args.batch_size

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

																																			_a     					=    self.evaluate(lowerCAmelCase_							,      lowerCAmelCase_ )
																																			_a     					=    {
																																			    '''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        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  List[str]							,      lowerCAmelCase_	:  str )      ->							List[str]:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  int							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  str )      ->							int:


														"""simple docstring"""


														_a     					=    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 		snake_case_   (UpperCamelCase	:  int			,						UpperCamelCase	:  Dict				):





							'''simple docstring'''


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

							with open(os.path.join(UpperCamelCase			,						'''opt_state.msgpack'''				)			,						'''rb'''				) as f:
														_a     					=    from_bytes(state.opt_state			,						f.read()				)

							_a     					=    joblib.load(os.path.join(UpperCamelCase			,						'''args.joblib'''				)				)
							_a     					=    joblib.load(os.path.join(UpperCamelCase			,						'''data_collator.joblib'''				)				)

							with open(os.path.join(UpperCamelCase			,						'''training_state.json'''				)			,						'''r'''				) as f:
														_a     					=    json.load(UpperCamelCase				)
							_a     					=    training_state['''step''']

							print('''DONE'''				)
							return params, opt_state, step, args, data_collator
def 		snake_case_   (UpperCamelCase	:  Dict			,						UpperCamelCase	:  Optional[int]			,						UpperCamelCase	:  Dict			,						UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


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

def 		snake_case_   (UpperCamelCase	:  Tuple			,						UpperCamelCase	:  Optional[Any]			,						UpperCamelCase	:  str			,						UpperCamelCase	:  Union[str, Any]			,						UpperCamelCase	:  int				):





							'''simple docstring'''


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

							_a     					=    scheduler_fn(UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase				)

							_a     					=    optax.adamw(learning_rate=UpperCamelCase			,						weight_decay=UpperCamelCase			,						mask=UpperCamelCase				)
							return tx, lr

