






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference

class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     str	     =ShapEImgaImgPipeline
    UpperCAmelCase_ :     int	     =["image"]
    UpperCAmelCase_ :     Tuple	     =["image"]
    UpperCAmelCase_ :     Union[str, Any]	     =[
        "num_images_per_prompt",
        "num_inference_steps",
        "generator",
        "latents",
        "guidance_scale",
        "frame_size",
        "output_type",
        "return_dict",
    ]
    UpperCAmelCase_ :     Union[str, Any]	     =False







    @property
    def 			UpperCamelCase_      (			self       ):
      return 3_2







    @property
    def 			UpperCamelCase_      (			self       ):
      return 3_2







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.time_input_dim * 4







    @property
    def 			UpperCamelCase_      (			self       ):
      return 8







    @property
    def 			UpperCamelCase_      (			self       ):
      torch.manual_seed(0       )
      lowercase							    =       CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size							,		image_size=6_4							,		projection_dim=self.text_embedder_hidden_size							,		intermediate_size=3_7							,		num_attention_heads=4							,		num_channels=3							,		num_hidden_layers=5							,		patch_size=1							,		)

      lowercase							    =       CLIPVisionModel(_lowerCamelCase       )
      return model







    @property
    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       CLIPImageProcessor(
          crop_size=2_2_4							,		do_center_crop=_lowerCamelCase							,		do_normalize=_lowerCamelCase							,		do_resize=_lowerCamelCase							,		image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]							,		image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]							,		resample=3							,		size=2_2_4							,		)

      return image_processor







    @property
    def 			UpperCamelCase_      (			self       ):
      torch.manual_seed(0       )

      lowercase							    =       {
          'num_attention_heads': 2,
          'attention_head_dim': 1_6,
          'embedding_dim': self.time_input_dim,
          'num_embeddings': 3_2,
          'embedding_proj_dim': self.text_embedder_hidden_size,
          'time_embed_dim': self.time_embed_dim,
          'num_layers': 1,
          'clip_embed_dim': self.time_input_dim * 2,
          'additional_embeddings': 0,
          'time_embed_act_fn': 'gelu',
          'norm_in_type': 'layer',
          'embedding_proj_norm_type': 'layer',
          'encoder_hid_proj_type': None,
          'added_emb_type': None,
      }

      lowercase							    =       PriorTransformer(**_lowerCamelCase       )
      return model







    @property
    def 			UpperCamelCase_      (			self       ):
      torch.manual_seed(0       )

      lowercase							    =       {
          'param_shapes': (
              (self.renderer_dim, 9_3),
              (self.renderer_dim, 8),
              (self.renderer_dim, 8),
              (self.renderer_dim, 8),
          ),
          'd_latent': self.time_input_dim,
          'd_hidden': self.renderer_dim,
          'n_output': 1_2,
          'background': (
              0.1,
              0.1,
              0.1,
          ),
      }
      lowercase							    =       ShapERenderer(**_lowerCamelCase       )
      return model







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.dummy_prior
      lowercase							    =       self.dummy_image_encoder
      lowercase							    =       self.dummy_image_processor
      lowercase							    =       self.dummy_renderer

      lowercase							    =       HeunDiscreteScheduler(
          beta_schedule='exp'							,		num_train_timesteps=1_0_2_4							,		prediction_type='sample'							,		use_karras_sigmas=_lowerCamelCase							,		clip_sample=_lowerCamelCase							,		clip_sample_range=1.0							,		)
      lowercase							    =       {
          'prior': prior,
          'image_encoder': image_encoder,
          'image_processor': image_processor,
          'renderer': renderer,
          'scheduler': scheduler,
      }

      return components







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=0       ):
      lowercase							    =       floats_tensor((1, 3, 6_4, 6_4)							,		rng=random.Random(_lowerCamelCase       )       ).to(_lowerCamelCase       )

      if str(_lowerCamelCase       ).startswith('mps'       ):
        lowercase							    =       torch.manual_seed(_lowerCamelCase       )
      else:
        lowercase							    =       torch.Generator(device=_lowerCamelCase       ).manual_seed(_lowerCamelCase       )
      lowercase							    =       {
          'image': input_image,
          'generator': generator,
          'num_inference_steps': 1,
          'frame_size': 3_2,
          'output_type': 'np',
      }
      return inputs







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'cpu'

      lowercase							    =       self.get_dummy_components()

      lowercase							    =       self.pipeline_class(**_lowerCamelCase       )
      lowercase							    =       pipe.to(_lowerCamelCase       )

      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

      lowercase							    =       pipe(**self.get_dummy_inputs(_lowerCamelCase       )       )
      lowercase							    =       output.images[0]
      lowercase							    =       image[0, -3:, -3:, -1]

      assert image.shape == (2_0, 3_2, 3_2, 3)

      lowercase							    =       np.array(
          [
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
              0.0_0_0_3_9_2_1_6,
          ]       )

      assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2







    def 			UpperCamelCase_      (			self       ):
      # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
      self._test_inference_batch_consistent(batch_sizes=[1, 2]       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch_device == 'cpu'
      lowercase							    =       True
      self._test_inference_batch_single_identical(
          batch_size=2							,		test_max_difference=_lowerCamelCase							,		relax_max_difference=_lowerCamelCase							,		)







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.get_dummy_components()
      lowercase							    =       self.pipeline_class(**_lowerCamelCase       )
      lowercase							    =       pipe.to(_lowerCamelCase       )
      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

      lowercase							    =       1
      lowercase							    =       2

      lowercase							    =       self.get_dummy_inputs(_lowerCamelCase       )

      for key in inputs.keys():
        if key in self.batch_params:
          lowercase							    =       batch_size * [inputs[key]]

      lowercase							    =       pipe(**_lowerCamelCase							,		num_images_per_prompt=_lowerCamelCase       )[0]

      assert images.shape[0] == batch_size * num_images_per_prompt


@slow
@require_torch_gpu
class        a     (   unittest.TestCase	):







    def 			UpperCamelCase_      (			self       ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'       )
      lowercase							    =       load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/shap_e/test_shap_e_img2img_out.npy'       )
      lowercase							    =       ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'       )
      lowercase							    =       pipe.to(_lowerCamelCase       )
      pipe.set_progress_bar_config(disable=_lowerCamelCase       )

      lowercase							    =       torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )

      lowercase							    =       pipe(
          _lowerCamelCase							,		generator=_lowerCamelCase							,		guidance_scale=3.0							,		num_inference_steps=6_4							,		frame_size=6_4							,		output_type='np'							,		).images[0]

      assert images.shape == (2_0, 6_4, 6_4, 3)

      assert_mean_pixel_difference(_lowerCamelCase							,		_lowerCamelCase       )










"""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_	):
    UpperCAmelCase_ :     BigBirdConfig
    UpperCAmelCase_ :     jnp.dtype	     =jnp.floataa
    UpperCAmelCase_ :     bool	     =True







    def 			UpperCamelCase_      (			self       ):
      super().setup()
      lowercase							    =       nn.Dense(5							,		dtype=self.dtype       )







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      lowercase							    =       super().__call__(*_lowerCamelCase							,		**_lowerCamelCase       )
      lowercase							    =       self.cls(outputs[2]       )
      return outputs[:2] + (cls_out,)


class        a     (   a_	):
    UpperCAmelCase_ :     str	     =FlaxBigBirdForNaturalQuestionsModule


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  Dict		,					__snake_case			:  Optional[Any]		,					__snake_case			:  Optional[int]		,					__snake_case			:  Tuple		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





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

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

@dataclass
class        a     :
    UpperCAmelCase_ :     str	     ="google/bigbird-roberta-base"
    UpperCAmelCase_ :     int	     =3000
    UpperCAmelCase_ :     int	     =1_0500

    UpperCAmelCase_ :     int	     =128
    UpperCAmelCase_ :     int	     =3

    UpperCAmelCase_ :     int	     =1
    UpperCAmelCase_ :     int	     =5

    # tx_args
    UpperCAmelCase_ :     float	     =3e-5
    UpperCAmelCase_ :     float	     =0.0
    UpperCAmelCase_ :     int	     =2_0000
    UpperCAmelCase_ :     float	     =0.00_95

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







    def 			UpperCamelCase_      (			self       ):
      os.makedirs(self.base_dir							,		exist_ok=_lowerCamelCase       )
      lowercase							    =       os.path.join(self.base_dir							,		self.save_dir       )
      lowercase							    =       self.batch_size_per_device * jax.device_count()


@dataclass
class        a     :
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int	     =4096  # no dynamic padding on TPUs







    def __call__(			self							,		_lowerCamelCase       ):
      lowercase							    =       self.collate_fn(_lowerCamelCase       )
      lowercase							    =       jax.tree_util.tree_map(_lowerCamelCase							,		_lowerCamelCase       )
      return batch







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     lowercase							    =       self.fetch_inputs(features['input_ids']       )
      lowercase							    =       {
          '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       ):
      lowercase							    =       [self._fetch_inputs(_lowerCamelCase       ) for ids in input_ids]
      return zip(*_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase							    =       [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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any		,					__snake_case			:  Tuple		,					__snake_case			:  Optional[Any]=None    ):





  '''simple docstring'''





  if seed is not None:
    lowercase							    =       dataset.shuffle(seed=__snake_case    )
  for i in range(len(__snake_case    ) // batch_size    ):
    lowercase							    =       dataset[i * batch_size : (i + 1) * batch_size]
    yield dict(__snake_case    )
@partial(jax.pmap		,					axis_name='batch'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Dict		,					__snake_case			:  List[Any]		,					**__snake_case			:  List[Any]    ):





  '''simple docstring'''





  def loss_fn(__snake_case			:  str    ):
    lowercase							    =       model_inputs.pop('start_labels'    )
    lowercase							    =       model_inputs.pop('end_labels'    )
    lowercase							    =       model_inputs.pop('pooled_labels'    )

    lowercase							    =       state.apply_fn(**__snake_case		,					params=__snake_case		,					dropout_rng=__snake_case		,					train=__snake_case    )
    lowercase    ,     lowercase    ,     lowercase							    =       outputs

    return state.loss_fn(
        __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					)

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

  lowercase							    =       state.apply_gradients(grads=__snake_case    )
  return state, metrics, new_drp_rng
@partial(jax.pmap		,					axis_name='batch'    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					**__snake_case			:  Dict    ):





  '''simple docstring'''





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

  lowercase							    =       state.apply_fn(**__snake_case		,					params=state.params		,					train=__snake_case    )
  lowercase    ,     lowercase    ,     lowercase							    =       outputs

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

class        a     (   train_state.TrainState	):
    UpperCAmelCase_ :     Callable	     =struct.field(pytree_node=a_	)


@dataclass
class        a     :
    UpperCAmelCase_ :     Args
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     Callable
    UpperCAmelCase_ :     wandb
    UpperCAmelCase_ :     Callable	     =None







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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       self.args
      lowercase							    =       len(_lowerCamelCase       ) // args.batch_size

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

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







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      lowercase							    =       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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  Tuple    ):





  '''simple docstring'''





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

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

  lowercase							    =       joblib.load(os.path.join(__snake_case		,					'args.joblib'    )    )
  lowercase							    =       joblib.load(os.path.join(__snake_case		,					'data_collator.joblib'    )    )

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

  print('DONE'    )
  return params, opt_state, step, args, data_collator
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  str		,					__snake_case			:  Any		,					__snake_case			:  Any    ):





  '''simple docstring'''





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



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]		,					__snake_case			:  str		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





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

  lowercase							    =       scheduler_fn(__snake_case		,					__snake_case		,					__snake_case		,					__snake_case    )

  lowercase							    =       optax.adamw(learning_rate=__snake_case		,					weight_decay=__snake_case		,					mask=__snake_case    )
  return tx, lr


