






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase     :							List[Any]    =16
lowerCamelCase     :							int    =32







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase = 16 ,   __lowerCAmelCase = "bert-base-cased"      )			->     Union[str, Any]:
     UpperCamelCase__      :       Tuple	    =  AutoTokenizer.from_pretrained(__lowerCAmelCase      )
     UpperCamelCase__      :       str	    =  load_dataset("glue" ,   "mrpc"      )

     def tokenize_function(__lowerCAmelCase      ):
          # max_length=None => use the model max length (it's actually the default)
          UpperCamelCase__      :       Dict	    =  tokenizer(examples["sentence1"] ,   examples["sentence2"] ,   truncation=__lowerCAmelCase ,   max_length=__lowerCAmelCase      )
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     UpperCamelCase__      :       Any	    =  datasets.map(
         __lowerCAmelCase ,   batched=__lowerCAmelCase ,   remove_columns=["idx", "sentence1", "sentence2"] ,   load_from_cache_file=__lowerCAmelCase      )

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     UpperCamelCase__      :       Union[str, Any]	    =  tokenized_datasets.rename_column("label" ,   "labels"      )

     def collate_fn(__lowerCAmelCase      ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(__lowerCAmelCase ,   padding="max_length" ,   max_length=128 ,   return_tensors="pt"      )
          return tokenizer.pad(__lowerCAmelCase ,   padding="longest" ,   return_tensors="pt"      )

     # Instantiate dataloaders.
     UpperCamelCase__      :       int	    =  DataLoader(
         tokenized_datasets["train"] ,   shuffle=__lowerCAmelCase ,   collate_fn=__lowerCAmelCase ,   batch_size=__lowerCAmelCase      )
     UpperCamelCase__      :       Tuple	    =  DataLoader(
         tokenized_datasets["validation"] ,   shuffle=__lowerCAmelCase ,   collate_fn=__lowerCAmelCase ,   batch_size=__lowerCAmelCase      )

     return train_dataloader, eval_dataloader







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[str]:
     # Initialize accelerator
     UpperCamelCase__      :       str	    =  Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCamelCase__      :       int	    =  config["lr"]
     UpperCamelCase__      :       Optional[Any]	    =  int(config["num_epochs"]      )
     UpperCamelCase__      :       Optional[Any]	    =  int(config["seed"]      )
     UpperCamelCase__      :       List[Any]	    =  int(config["batch_size"]      )
     UpperCamelCase__      :       Dict	    =  args.model_name_or_path

     set_seed(__lowerCAmelCase      )
     UpperCamelCase__    ,					UpperCamelCase__      :       List[str]	    =  get_dataloaders(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCamelCase__      :       List[Any]	    =  AutoModelForSequenceClassification.from_pretrained(__lowerCAmelCase ,   return_dict=__lowerCAmelCase      )

     # Instantiate optimizer
     UpperCamelCase__      :       List[str]	    =  (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     UpperCamelCase__      :       Tuple	    =  optimizer_cls(params=model.parameters() ,   lr=__lowerCAmelCase      )

     if accelerator.state.deepspeed_plugin is not None:
          UpperCamelCase__      :       Union[str, Any]	    =  accelerator.state.deepspeed_plugin.deepspeed_config[
              "gradient_accumulation_steps"
          ]
     else:
          UpperCamelCase__      :       List[Any]	    =  1
     UpperCamelCase__      :       Optional[int]	    =  (len(__lowerCAmelCase      ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          UpperCamelCase__      :       List[Any]	    =  get_linear_schedule_with_warmup(
              optimizer=__lowerCAmelCase ,   num_warmup_steps=0 ,   num_training_steps=__lowerCAmelCase ,   )
     else:
          UpperCamelCase__      :       Tuple	    =  DummyScheduler(__lowerCAmelCase ,   total_num_steps=__lowerCAmelCase ,   warmup_num_steps=0      )

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__    ,					UpperCamelCase__      :       Any	    =  accelerator.prepare(
         __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )

     # We need to keep track of how many total steps we have iterated over
     UpperCamelCase__      :       Union[str, Any]	    =  0
     # We also need to keep track of the stating epoch so files are named properly
     UpperCamelCase__      :       str	    =  0

     # Now we train the model
     UpperCamelCase__      :       List[Any]	    =  evaluate.load("glue" ,   "mrpc"      )
     UpperCamelCase__      :       Optional[int]	    =  0
     UpperCamelCase__      :       int	    =  {}
     for epoch in range(__lowerCAmelCase ,   __lowerCAmelCase      ):
          model.train()
          for step, batch in enumerate(__lowerCAmelCase      ):
               UpperCamelCase__      :       str	    =  model(**__lowerCAmelCase      )
               UpperCamelCase__      :       Any	    =  outputs.loss
               UpperCamelCase__      :       List[str]	    =  loss / gradient_accumulation_steps
               accelerator.backward(__lowerCAmelCase      )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

          model.eval()
          UpperCamelCase__      :       List[str]	    =  0
          for step, batch in enumerate(__lowerCAmelCase      ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device      )
               with torch.no_grad():
                    UpperCamelCase__      :       Any	    =  model(**__lowerCAmelCase      )
               UpperCamelCase__      :       Dict	    =  outputs.logits.argmax(dim=-1      )
               # It is slightly faster to call this once, than multiple times
               UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  accelerator.gather(
                   (predictions, batch["labels"])      )  # If we are in a multiprocess environment, the last batch has duplicates
               if accelerator.use_distributed:
                    if step == len(__lowerCAmelCase      ) - 1:
                         UpperCamelCase__      :       Tuple	    =  predictions[: len(eval_dataloader.dataset      ) - samples_seen]
                         UpperCamelCase__      :       Optional[int]	    =  references[: len(eval_dataloader.dataset      ) - samples_seen]
                    else:
                         samples_seen += references.shape[0]
               metric.add_batch(
                   predictions=__lowerCAmelCase ,   references=__lowerCAmelCase ,   )

          UpperCamelCase__      :       int	    =  metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:' ,   __lowerCAmelCase      )
          UpperCamelCase__      :       int	    =  eval_metric["accuracy"]

          if best_performance < eval_metric["accuracy"]:
               UpperCamelCase__      :       Dict	    =  eval_metric["accuracy"]

     if args.performance_lower_bound is not None:
          assert (
              args.performance_lower_bound <= best_performance
          ), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
          with open(os.path.join(args.output_dir ,   "all_results.json"      ) ,   "w"      ) as f:
               json.dump(__lowerCAmelCase ,   __lowerCAmelCase      )







def 		SCREAMING_SNAKE_CASE							( )			->     Union[str, Any]:
     UpperCamelCase__      :       str	    =  argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
     parser.add_argument(
         "--model_name_or_path" ,   type=__lowerCAmelCase ,   default="bert-base-cased" ,   help="Path to pretrained model or model identifier from huggingface.co/models." ,   required=__lowerCAmelCase ,   )
     parser.add_argument(
         "--output_dir" ,   type=__lowerCAmelCase ,   default="." ,   help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." ,   )
     parser.add_argument(
         "--performance_lower_bound" ,   type=__lowerCAmelCase ,   default=__lowerCAmelCase ,   help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value." ,   )
     parser.add_argument(
         "--num_epochs" ,   type=__lowerCAmelCase ,   default=3 ,   help="Number of train epochs." ,   )
     UpperCamelCase__      :       Tuple	    =  parser.parse_args()
     UpperCamelCase__      :       List[Any]	    =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
     training_function(__lowerCAmelCase ,   __lowerCAmelCase      )


if __name__ == "__main__":
 main()







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)





@flax.struct.dataclass
class       __a  :
 _lowerCAmelCase	:	CommonSchedulerState

 # setable values
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	jnp.ndarray
 _lowerCAmelCase	:	Optional[int]				=			None







 @classmethod
 def  __lowercase (		cls :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       CommonSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray						):


      '''simple docstring'''






      return cls(common=SCREAMING_SNAKE_CASE							,       init_noise_sigma=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE						)







@dataclass
class       __a  (   A__	):
 _lowerCAmelCase	:	DDPMSchedulerState







class       __a  (   A__	,       A__	):
 _lowerCAmelCase	:	Tuple				=			[e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCAmelCase	:	jnp.dtype







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


      '''simple docstring'''






      return True







 @register_to_config
 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       int = 10_00							,       SCREAMING_SNAKE_CASE :       float = 0.0_0_0_1							,       SCREAMING_SNAKE_CASE :       float = 0.0_2							,       SCREAMING_SNAKE_CASE :       str = "linear"							,       SCREAMING_SNAKE_CASE :       Optional[jnp.ndarray] = None							,       SCREAMING_SNAKE_CASE :       str = "fixed_small"							,       SCREAMING_SNAKE_CASE :       bool = True							,       SCREAMING_SNAKE_CASE :       str = "epsilon"							,       SCREAMING_SNAKE_CASE :       jnp.dtype = jnp.floataa							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  dtype







 def  __lowercase (		self :       List[Any]							,       SCREAMING_SNAKE_CASE :       Optional[CommonSchedulerState] = None						):


      '''simple docstring'''






      if common is None:
           UpperCamelCase__      :       Tuple	    =  CommonSchedulerState.create(self						)

      # standard deviation of the initial noise distribution
      UpperCamelCase__      :       str	    =  jnp.array(1.0							,       dtype=self.dtype						)

      UpperCamelCase__      :       Any	    =  jnp.arange(0							,       self.config.num_train_timesteps						).round()[::-1]

      return DDPMSchedulerState.create(
          common=SCREAMING_SNAKE_CASE							,       init_noise_sigma=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE							,       )







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[int] = None						):


      '''simple docstring'''






      return sample







 def  __lowercase (		self :       Tuple							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       Tuple = ()						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  self.config.num_train_timesteps // num_inference_steps
      # creates integer timesteps by multiplying by ratio
      # rounding to avoid issues when num_inference_step is power of 3
      UpperCamelCase__      :       Optional[int]	    =  (jnp.arange(0							,       SCREAMING_SNAKE_CASE						) * step_ratio).round()[::-1]

      return state.replace(
          num_inference_steps=SCREAMING_SNAKE_CASE							,       timesteps=SCREAMING_SNAKE_CASE							,       )







 def  __lowercase (		self :       str							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       List[str]							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       Optional[Any]=None						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Tuple	    =  jnp.where(t > 0							,       state.common.alphas_cumprod[t - 1]							,       jnp.array(1.0							,       dtype=self.dtype						)						)

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      UpperCamelCase__      :       int	    =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

      if variance_type is None:
           UpperCamelCase__      :       List[Any]	    =  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small":
           UpperCamelCase__      :       Union[str, Any]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)
      # for rl-diffuser https://arxiv.org/abs/2205.09991
      elif variance_type == "fixed_small_log":
           UpperCamelCase__      :       Optional[Any]	    =  jnp.log(jnp.clip(SCREAMING_SNAKE_CASE							,       a_min=1e-2_0						)						)
      elif variance_type == "fixed_large":
           UpperCamelCase__      :       Dict	    =  state.common.betas[t]
      elif variance_type == "fixed_large_log":
           # Glide max_log
           UpperCamelCase__      :       List[str]	    =  jnp.log(state.common.betas[t]						)
      elif variance_type == "learned":
           return predicted_variance
      elif variance_type == "learned_range":
           UpperCamelCase__      :       Tuple	    =  variance
           UpperCamelCase__      :       int	    =  state.common.betas[t]
           UpperCamelCase__      :       Union[str, Any]	    =  (predicted_variance + 1) / 2
           UpperCamelCase__      :       List[str]	    =  frac * max_log + (1 - frac) * min_log

      return variance







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       int							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       Optional[jax.random.KeyArray] = None							,       SCREAMING_SNAKE_CASE :       bool = True							,       ):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  timestep

      if key is None:
           UpperCamelCase__      :       str	    =  jax.random.PRNGKey(0						)

      if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
           UpperCamelCase__    ,					UpperCamelCase__      :       Optional[int]	    =  jnp.split(SCREAMING_SNAKE_CASE							,       sample.shape[1]							,       axis=1						)
      else:
           UpperCamelCase__      :       Optional[int]	    =  None

      # 1. compute alphas, betas
      UpperCamelCase__      :       Optional[int]	    =  state.common.alphas_cumprod[t]
      UpperCamelCase__      :       Dict	    =  jnp.where(t > 0							,       state.common.alphas_cumprod[t - 1]							,       jnp.array(1.0							,       dtype=self.dtype						)						)
      UpperCamelCase__      :       Any	    =  1 - alpha_prod_t
      UpperCamelCase__      :       List[str]	    =  1 - alpha_prod_t_prev

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
           UpperCamelCase__      :       Optional[Any]	    =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           UpperCamelCase__      :       Dict	    =  model_output
      elif self.config.prediction_type == "v_prediction":
           UpperCamelCase__      :       str	    =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
      else:
           raise ValueError(
               F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '
               " for the FlaxDDPMScheduler."						)

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
           UpperCamelCase__      :       List[str]	    =  jnp.clip(SCREAMING_SNAKE_CASE							,       -1							,       1						)

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCamelCase__      :       List[Any]	    =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      UpperCamelCase__      :       str	    =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      UpperCamelCase__      :       Any	    =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      def random_variance():
           UpperCamelCase__      :       str	    =  jax.random.split(SCREAMING_SNAKE_CASE							,       num=1						)
           UpperCamelCase__      :       Tuple	    =  jax.random.normal(SCREAMING_SNAKE_CASE							,       shape=model_output.shape							,       dtype=self.dtype						)
           return (self._get_variance(SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       predicted_variance=SCREAMING_SNAKE_CASE						) ** 0.5) * noise

      UpperCamelCase__      :       Any	    =  jnp.where(t > 0							,       random_variance()							,       jnp.zeros(model_output.shape							,       dtype=self.dtype						)						)

      UpperCamelCase__      :       List[str]	    =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE							,       state=SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Any							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return add_noise_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def  __lowercase (		self :       Dict							,       SCREAMING_SNAKE_CASE :       DDPMSchedulerState							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       SCREAMING_SNAKE_CASE :       jnp.ndarray							,       ):


      '''simple docstring'''






      return get_velocity_common(state.common							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE							,       SCREAMING_SNAKE_CASE						)







 def __len__(		self :       int						):


      '''simple docstring'''






      return self.config.num_train_timesteps