





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        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  jnp.floataa
     __UpperCamelCase            =  True



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




        super().setup()
        A_       :					Optional[int]        =      nn.Dense(5      ,   dtype=self.dtype	)





     def __call__(				self							:List[str]      ,   *snake_case							:List[str]      ,   **snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Any        =      super().__call__(*snake_case      ,   **snake_case	)
        A_       :					Optional[int]        =      self.cls(outputs[2]	)
        return outputs[:2] + (cls_out,)




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  FlaxBigBirdForNaturalQuestionsModule




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

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


@dataclass
class        __magic_name__	:





     """simple docstring"""



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

     __UpperCamelCase            =  1_28
     __UpperCamelCase            =  3

     __UpperCamelCase            =  1
     __UpperCamelCase            =  5

     # tx_args
     __UpperCamelCase            =  3e-5
     __UpperCamelCase            =  0.0
     __UpperCamelCase            =  2_00_00
     __UpperCamelCase            =  0.0095

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



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        os.makedirs(self.base_dir      ,   exist_ok=snake_case	)
        A_       :					Optional[Any]        =      os.path.join(self.base_dir      ,   self.save_dir	)
        A_       :					int        =      self.batch_size_per_device * jax.device_count()




@dataclass
class        __magic_name__	:





     """simple docstring"""



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



     def __call__(				self							:List[str]      ,   snake_case							:Union[str, Any]	):





        '''simple docstring'''




        A_       :					Any        =      self.collate_fn(snake_case	)
        A_       :					int        =      jax.tree_util.tree_map(snake_case      ,   snake_case	)
        return batch



     def     SCREAMING_SNAKE_CASE		(				self							:List[str]      ,   snake_case							:Optional[Any]	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:list	):





        '''simple docstring'''




        A_       :					str        =      [self._fetch_inputs(snake_case	) for ids in input_ids]
        return zip(*snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:list	):





        '''simple docstring'''




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




def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			Tuple=None						)			->    List[str]:
   if seed is not None:
      A_       :					Any        =      dataset.shuffle(seed=_lowerCAmelCase						)
   for i in range(len(_lowerCAmelCase						) // batch_size						):
      A_       :					List[Any]        =      dataset[i * batch_size : (i + 1) * batch_size]
      yield dict(_lowerCAmelCase						)

@partial(jax.pmap					,					axis_name="batch"						)
def   __snake_case    (       _lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			Union[str, Any]					,					**_lowerCAmelCase			:			str						)			->    List[Any]:
   def loss_fn(_lowerCAmelCase			:			int						):
      A_       :					int        =      model_inputs.pop("start_labels"						)
      A_       :					Tuple        =      model_inputs.pop("end_labels"						)
      A_       :					List[Any]        =      model_inputs.pop("pooled_labels"						)

      A_       :					int        =      state.apply_fn(**_lowerCAmelCase					,					params=_lowerCAmelCase					,					dropout_rng=_lowerCAmelCase					,					train=_lowerCAmelCase						)
      A_   ,    A_   ,    A_       :					int        =      outputs

      return state.loss_fn(
          _lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					)

   A_   ,    A_       :					List[str]        =      jax.random.split(_lowerCAmelCase						)
   A_       :					Optional[int]        =      jax.value_and_grad(_lowerCAmelCase						)
   A_   ,    A_       :					Dict        =      grad_fn(state.params						)
   A_       :					List[str]        =      jax.lax.pmean({"loss": loss}					,					axis_name="batch"						)
   A_       :					List[str]        =      jax.lax.pmean(_lowerCAmelCase					,					"batch"						)

   A_       :					str        =      state.apply_gradients(grads=_lowerCAmelCase						)
   return state, metrics, new_drp_rng

@partial(jax.pmap					,					axis_name="batch"						)
def   __snake_case    (       _lowerCAmelCase			:			Optional[int]					,					**_lowerCAmelCase			:			Optional[Any]						)			->    Optional[Any]:
   A_       :					List[str]        =      model_inputs.pop("start_labels"						)
   A_       :					Optional[int]        =      model_inputs.pop("end_labels"						)
   A_       :					Optional[Any]        =      model_inputs.pop("pooled_labels"						)

   A_       :					str        =      state.apply_fn(**_lowerCAmelCase					,					params=state.params					,					train=_lowerCAmelCase						)
   A_   ,    A_   ,    A_       :					Optional[Any]        =      outputs

   A_       :					Dict        =      state.loss_fn(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)
   A_       :					int        =      jax.lax.pmean({"loss": loss}					,					axis_name="batch"						)
   return metrics


class        __magic_name__	(				train_state.TrainState				):





     """simple docstring"""



     __UpperCamelCase            =  struct.field(pytree_node=lowerCamelCase__				)




@dataclass
class        __magic_name__	:





     """simple docstring"""



     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  42
     __UpperCamelCase            =  None



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Tuple      ,   snake_case							:Union[str, Any]      ,   snake_case							:Optional[int]      ,   snake_case							:Optional[int]=None	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:Optional[int]      ,   snake_case							:List[Any]      ,   snake_case							:List[str]	):





        '''simple docstring'''




        A_       :					Optional[Any]        =      self.args
        A_       :					Any        =      len(snake_case	) // args.batch_size

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

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

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



     def     SCREAMING_SNAKE_CASE		(				self							:str      ,   snake_case							:Union[str, Any]      ,   snake_case							:str	):





        '''simple docstring'''




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





     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]      ,   snake_case							:Tuple      ,   snake_case							:Optional[int]	):





        '''simple docstring'''




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




def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]					,					_lowerCAmelCase			:			str						)			->    Tuple:
   print(f"RESTORING CHECKPOINT FROM {save_dir}"					,					end=" ... "						)
   with open(os.path.join(_lowerCAmelCase					,					"flax_model.msgpack"						)					,					"rb"						) as f:
      A_       :					List[Any]        =      from_bytes(state.params					,					f.read()						)

   with open(os.path.join(_lowerCAmelCase					,					"opt_state.msgpack"						)					,					"rb"						) as f:
      A_       :					Union[str, Any]        =      from_bytes(state.opt_state					,					f.read()						)

   A_       :					int        =      joblib.load(os.path.join(_lowerCAmelCase					,					"args.joblib"						)						)
   A_       :					List[str]        =      joblib.load(os.path.join(_lowerCAmelCase					,					"data_collator.joblib"						)						)

   with open(os.path.join(_lowerCAmelCase					,					"training_state.json"						)					,					"r"						) as f:
      A_       :					Optional[int]        =      json.load(_lowerCAmelCase						)
   A_       :					str        =      training_state["step"]

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

def   __snake_case    (       _lowerCAmelCase			:			List[Any]					,					_lowerCAmelCase			:			int					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			Any						)			->    Any:
   A_       :					int        =      num_train_steps - warmup_steps
   A_       :					Tuple        =      optax.linear_schedule(init_value=_lowerCAmelCase					,					end_value=_lowerCAmelCase					,					transition_steps=_lowerCAmelCase						)
   A_       :					Optional[int]        =      optax.linear_schedule(init_value=_lowerCAmelCase					,					end_value=1e-7					,					transition_steps=_lowerCAmelCase						)
   A_       :					Optional[int]        =      optax.join_schedules(schedules=[warmup_fn, decay_fn]					,					boundaries=[warmup_steps]						)
   return lr





def   __snake_case    (       _lowerCAmelCase			:			int					,					_lowerCAmelCase			:			str					,					_lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			Optional[int]					,					_lowerCAmelCase			:			int						)			->    Dict:
   def weight_decay_mask(_lowerCAmelCase			:			Union[str, Any]						):
      A_       :					Optional[Any]        =      traverse_util.flatten_dict(_lowerCAmelCase						)
      A_       :					List[str]        =      {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
      return traverse_util.unflatten_dict(_lowerCAmelCase						)

   A_       :					str        =      scheduler_fn(_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase					,					_lowerCAmelCase						)

   A_       :					Any        =      optax.adamw(learning_rate=_lowerCAmelCase					,					weight_decay=_lowerCAmelCase					,					mask=_lowerCAmelCase						)
   return tx, lr







import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Optional[Any]        =      UNetaDModel(
            block_out_channels=(32, 64)      ,   layers_per_block=2      ,   sample_size=32      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=("DownBlock2D", "AttnDownBlock2D")      ,   up_block_types=("AttnUpBlock2D", "UpBlock2D")      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:int	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					Any        =      VQModel(
            block_out_channels=[32, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,   latent_channels=3      ,   )
        return model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        torch.manual_seed(0	)
        A_       :					int        =      CLIPTextConfig(
            bos_token_id=0      ,   eos_token_id=2      ,   hidden_size=32      ,   intermediate_size=37      ,   layer_norm_eps=1e-05      ,   num_attention_heads=4      ,   num_hidden_layers=5      ,   pad_token_id=1      ,   vocab_size=1_000      ,   )
        return CLIPTextModel(snake_case	)





     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      self.dummy_uncond_unet
        A_       :					Any        =      DDIMScheduler()
        A_       :					List[str]        =      self.dummy_vq_model

        A_       :					Union[str, Any]        =      LDMPipeline(unet=snake_case      ,   vqvae=snake_case      ,   scheduler=snake_case	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					Any        =      torch.manual_seed(0	)
        A_       :					Optional[Any]        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"	).images

        A_       :					Dict        =      torch.manual_seed(0	)
        A_       :					Any        =      ldm(generator=snake_case      ,   num_inference_steps=2      ,   output_type="numpy"      ,   return_dict=snake_case	)[0]

        A_       :					int        =      image[0, -3:, -3:, -1]
        A_       :					Any        =      image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        A_       :					str        =      np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]	)
        A_       :					List[str]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance




@slow
@require_torch
class        __magic_name__	(				unittest.TestCase				):





     """simple docstring"""



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        A_       :					int        =      LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256"	)
        ldm.to(snake_case	)
        ldm.set_progress_bar_config(disable=snake_case	)

        A_       :					int        =      torch.manual_seed(0	)
        A_       :					List[str]        =      ldm(generator=snake_case      ,   num_inference_steps=5      ,   output_type="numpy"	).images

        A_       :					Tuple        =      image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        A_       :					Any        =      np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447]	)
        A_       :					List[Any]        =      1e-2 if torch_device != "mps" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
