




'''simple docstring'''




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
     import torch


a_      :		Tuple							      =       logging.get_logger(__name__)


class   __UpperCamelCase       ( SCREAMING_SNAKE_CASE__				):
       lowercase	:    Union[str, Any]      						=['pixel_values']






       def __init__(							self,		lowerCAmelCase = True,		lowerCAmelCase = None,		lowerCAmelCase = PILImageResampling.BILINEAR,		lowerCAmelCase = True,		lowerCAmelCase = None,		lowerCAmelCase = True,		lowerCAmelCase = 1 / 255,		lowerCAmelCase = True,		lowerCAmelCase = None,		lowerCAmelCase = None,		**lowerCAmelCase,		):



              """simple docstring"""







              super().__init__(**__lowerCamelCase  )
              lowerCamelCase_							=size if size is not None else {'''shortest_edge''': 256}
              lowerCamelCase_							=get_size_dict(__lowerCamelCase,		default_to_square=__lowerCamelCase  )
              lowerCamelCase_							=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
              lowerCamelCase_							=get_size_dict(__lowerCamelCase,		param_name='''crop_size'''  )
              lowerCamelCase_							=do_resize
              lowerCamelCase_							=size
              lowerCamelCase_							=resample
              lowerCamelCase_							=do_center_crop
              lowerCamelCase_							=crop_size
              lowerCamelCase_							=do_rescale
              lowerCamelCase_							=rescale_factor
              lowerCamelCase_							=do_normalize
              lowerCamelCase_							=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              lowerCamelCase_							=image_std if image_std is not None else IMAGENET_STANDARD_STD






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = PILImageResampling.BICUBIC,		lowerCAmelCase = None,		**lowerCAmelCase,		):



              """simple docstring"""







              lowerCamelCase_							=get_size_dict(__lowerCamelCase,		default_to_square=__lowerCamelCase  )
              if "shortest_edge" not in size:
                     raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
              lowerCamelCase_							=get_resize_output_image_size(__lowerCamelCase,		size=size['''shortest_edge'''],		default_to_square=__lowerCamelCase  )
              return resize(__lowerCamelCase,		size=__lowerCamelCase,		resample=__lowerCamelCase,		data_format=__lowerCamelCase,		**__lowerCamelCase  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = None,		**lowerCAmelCase,		):



              """simple docstring"""







              lowerCamelCase_							=get_size_dict(__lowerCamelCase  )
              if "height" not in size or "width" not in size:
                     raise ValueError(f'''The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'''  )
              return center_crop(__lowerCamelCase,		size=(size['''height'''], size['''width''']),		data_format=__lowerCamelCase,		**__lowerCamelCase  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = None,		**lowerCAmelCase  ):



              """simple docstring"""







              return rescale(__lowerCamelCase,		scale=__lowerCamelCase,		data_format=__lowerCamelCase,		**__lowerCamelCase  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase = None,		**lowerCAmelCase,		):



              """simple docstring"""







              return normalize(__lowerCamelCase,		mean=__lowerCamelCase,		std=__lowerCamelCase,		data_format=__lowerCamelCase,		**__lowerCamelCase  )






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = None,		lowerCAmelCase = ChannelDimension.FIRST,		**lowerCAmelCase,		):



              """simple docstring"""







              lowerCamelCase_							=do_resize if do_resize is not None else self.do_resize
              lowerCamelCase_							=size if size is not None else self.size
              lowerCamelCase_							=get_size_dict(__lowerCamelCase,		default_to_square=__lowerCamelCase  )
              lowerCamelCase_							=resample if resample is not None else self.resample
              lowerCamelCase_							=do_center_crop if do_center_crop is not None else self.do_center_crop
              lowerCamelCase_							=crop_size if crop_size is not None else self.crop_size
              lowerCamelCase_							=get_size_dict(__lowerCamelCase,		param_name='''crop_size'''  )
              lowerCamelCase_							=do_rescale if do_rescale is not None else self.do_rescale
              lowerCamelCase_							=rescale_factor if rescale_factor is not None else self.rescale_factor
              lowerCamelCase_							=do_normalize if do_normalize is not None else self.do_normalize
              lowerCamelCase_							=image_mean if image_mean is not None else self.image_mean
              lowerCamelCase_							=image_std if image_std is not None else self.image_std

              lowerCamelCase_							=make_list_of_images(__lowerCamelCase  )

              if not valid_images(__lowerCamelCase  ):
                     raise ValueError(
                         '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                         '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

              if do_resize and size is None:
                     raise ValueError('''Size must be specified if do_resize is True.'''  )

              if do_center_crop and crop_size is None:
                     raise ValueError('''Crop size must be specified if do_center_crop is True.'''  )

              if do_rescale and rescale_factor is None:
                     raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

              if do_normalize and (image_mean is None or image_std is None):
                     raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

              # All transformations expect numpy arrays.
              lowerCamelCase_							=[to_numpy_array(__lowerCamelCase  ) for image in images]

              if do_resize:
                     lowerCamelCase_							=[self.resize(image=__lowerCamelCase,		size=__lowerCamelCase,		resample=__lowerCamelCase  ) for image in images]

              if do_center_crop:
                     lowerCamelCase_							=[self.center_crop(image=__lowerCamelCase,		size=__lowerCamelCase  ) for image in images]

              if do_rescale:
                     lowerCamelCase_							=[self.rescale(image=__lowerCamelCase,		scale=__lowerCamelCase  ) for image in images]

              if do_normalize:
                     lowerCamelCase_							=[self.normalize(image=__lowerCamelCase,		mean=__lowerCamelCase,		std=__lowerCamelCase  ) for image in images]

              lowerCamelCase_							=[to_channel_dimension_format(__lowerCamelCase,		__lowerCamelCase  ) for image in images]

              lowerCamelCase_							={'''pixel_values''': images}
              return BatchFeature(data=__lowerCamelCase,		tensor_type=__lowerCamelCase  )






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



              """simple docstring"""







              lowerCamelCase_							=outputs.logits

              # Resize logits and compute semantic segmentation maps
              if target_sizes is not None:
                     if len(__lowerCamelCase  ) != len(__lowerCamelCase  ):
                            raise ValueError(
                                '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''  )

                     if is_torch_tensor(__lowerCamelCase  ):
                            lowerCamelCase_							=target_sizes.numpy()

                     lowerCamelCase_							=[]

                     for idx in range(len(__lowerCamelCase  )  ):
                            lowerCamelCase_							=torch.nn.functional.interpolate(
                                logits[idx].unsqueeze(dim=0  ),		size=target_sizes[idx],		mode='''bilinear''',		align_corners=__lowerCamelCase  )
                            lowerCamelCase_							=resized_logits[0].argmax(dim=0  )
                            semantic_segmentation.append(__lowerCamelCase  )
              else:
                     lowerCamelCase_							=logits.argmax(dim=1  )
                     lowerCamelCase_							=[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

              return semantic_segmentation











"""simple docstring"""



import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42
 _lowerCamelCase        =     jnp.floataa
 _lowerCamelCase        =     True

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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







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



     '''simple docstring'''




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






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     FlaxBigBirdForNaturalQuestionsModule







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



    '''simple docstring'''







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

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






@dataclass
class 		__snake_case					:





 """simple docstring"""


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

 _lowerCamelCase        =     1_28
 _lowerCamelCase        =     3

 _lowerCamelCase        =     1
 _lowerCamelCase        =     5

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

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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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






@dataclass
class 		__snake_case					:





 """simple docstring"""


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

 def __call__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     __A ,       __A    :						List[Any]						 =     self.fetch_inputs(features['''input_ids''']  )
     __A    :						Union[str, Any]						 =     {
         '''input_ids''': jnp.array(__lowerCamelCase					,						dtype=jnp.intaa  ),
         '''attention_mask''': jnp.array(__lowerCamelCase					,						dtype=jnp.intaa  ),
         '''start_labels''': jnp.array(features['''start_token''']					,						dtype=jnp.intaa  ),
         '''end_labels''': jnp.array(features['''end_token''']					,						dtype=jnp.intaa  ),
         '''pooled_labels''': jnp.array(features['''category''']					,						dtype=jnp.intaa  ),
     }
     return batch

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




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







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



    '''simple docstring'''







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







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



    '''simple docstring'''







    def loss_fn(snake_case_  :  List[str]						):
        __A    :						str						 =     model_inputs.pop('''start_labels'''						)
        __A    :						str						 =     model_inputs.pop('''end_labels'''						)
        __A    :						int						 =     model_inputs.pop('''pooled_labels'''						)

        __A    :						Dict						 =     state.apply_fn(**snake_case_				,params=snake_case_				,dropout_rng=snake_case_				,train=snake_case_						)
        __A ,       __A ,       __A    :						Union[str, Any]						 =     outputs

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

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

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







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



    '''simple docstring'''







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

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

    __A    :						Optional[int]						 =     state.loss_fn(snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_				,snake_case_						)
    __A    :						List[str]						 =     jax.lax.pmean({'''loss''': loss}				,axis_name='''batch'''						)
    return metrics






class 		__snake_case					(		train_state.TrainState     ):





 """simple docstring"""


 _lowerCamelCase        =     struct.field(pytree_node=SCREAMING_SNAKE_CASE__     )






@dataclass
class 		__snake_case					:





 """simple docstring"""


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

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



     '''simple docstring'''




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

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



     '''simple docstring'''




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

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

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

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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







 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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







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



    '''simple docstring'''







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

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

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

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

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







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



    '''simple docstring'''







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







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



    '''simple docstring'''







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

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

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




