






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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					42
     SCREAMING_SNAKE_CASE     =					None



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Union[str, Any]=0.999      ,					__lowercase :      List[Any]="cosine"      ,					)    ->   Optional[int]:





    """simple docstring"""

    if alpha_transform_type == "cosine":

        def alpha_bar_fn(__lowercase :      Optional[Any]     ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(__lowercase :      Dict     ):
            return math.exp(t * -12.0     )

    else:
        raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}"     )

    __A       =						[]
    for i in range(__lowercase     ):
        __A       =						i / num_diffusion_timesteps
        __A       =						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__lowercase     ) / alpha_bar_fn(__lowercase     )      ,					__lowercase     )     )
    return torch.tensor(__lowercase      ,					dtype=torch.floataa     )






class    __lowercase   (						lowercase_      ,   lowercase_	):







     '''simple docstring'''




     @register_to_config
     def __init__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      int = 1_000			,						UpperCamelCase_							:      str = "fixed_small_log"			,						UpperCamelCase_							:      bool = True			,						UpperCamelCase_							:      Optional[float] = 1.0			,						UpperCamelCase_							:      str = "epsilon"			,						UpperCamelCase_							:      str = "squaredcos_cap_v2"			,						):


         """simple docstring"""



         if beta_schedule != "squaredcos_cap_v2":
             raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""					)

         __A       =						betas_for_alpha_bar(UpperCamelCase_					)

         __A       =						1.0 - self.betas
         __A       =						torch.cumprod(self.alphas			,						dim=0					)
         __A       =						torch.tensor(1.0					)

         # standard deviation of the initial noise distribution
         __A       =						1.0

         # setable values
         __A       =						None
         __A       =						torch.from_numpy(np.arange(0			,						UpperCamelCase_					)[::-1].copy()					)

         __A       =						variance_type




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      torch.FloatTensor			,						UpperCamelCase_							:      Optional[int] = None					):


         """simple docstring"""



         return sample




     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Union[str, torch.device] = None					):


         """simple docstring"""



         __A       =						num_inference_steps
         __A       =						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         __A       =						(np.arange(0			,						UpperCamelCase_					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
         __A       =						torch.from_numpy(UpperCamelCase_					).to(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      List[str]=None			,						UpperCamelCase_							:      str=None			,						UpperCamelCase_							:      Optional[int]=None					):


         """simple docstring"""



         if prev_timestep is None:
             __A       =						t - 1

         __A       =						self.alphas_cumprod[t]
         __A       =						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __A       =						1 - alpha_prod_t
         __A       =						1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
             __A       =						self.betas[t]
         else:
             __A       =						1 - alpha_prod_t / alpha_prod_t_prev

         # 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
         __A       =						beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
             __A       =						self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
             __A       =						torch.log(torch.clamp(UpperCamelCase_			,						min=1e-20					)					)
             __A       =						torch.exp(0.5 * variance					)
         elif variance_type == "learned_range":
             # NOTE difference with DDPM scheduler
             __A       =						variance.log()
             __A       =						beta.log()

             __A       =						(predicted_variance + 1) / 2
             __A       =						frac * max_log + (1 - frac) * min_log

         return variance




     def 		lowerCAmelCase_  (					self							:      Any			,						UpperCamelCase_							:      torch.FloatTensor			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      torch.FloatTensor			,						UpperCamelCase_							:      Optional[int] = None			,						UpperCamelCase_							:      List[str]=None			,						UpperCamelCase_							:      bool = True			,						):


         """simple docstring"""



         __A       =						timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
             __A     ,						__A       =						torch.split(UpperCamelCase_			,						sample.shape[1]			,						dim=1					)
         else:
             __A       =						None

         # 1. compute alphas, betas
         if prev_timestep is None:
             __A       =						t - 1

         __A       =						self.alphas_cumprod[t]
         __A       =						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         __A       =						1 - alpha_prod_t
         __A       =						1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
             __A       =						self.betas[t]
             __A       =						self.alphas[t]
         else:
             __A       =						1 - alpha_prod_t / alpha_prod_t_prev
             __A       =						1 - beta

         # 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":
             __A       =						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
             __A       =						model_output
         else:
             raise ValueError(
                 F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
                 """ for the UnCLIPScheduler."""					)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
             __A       =						torch.clamp(
                 UpperCamelCase_			,						-self.config.clip_sample_range			,						self.config.clip_sample_range					)

         # 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
         __A       =						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         __A       =						alpha ** 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
         __A       =						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         __A       =						0
         if t > 0:
             __A       =						randn_tensor(
                 model_output.shape			,						dtype=model_output.dtype			,						generator=UpperCamelCase_			,						device=model_output.device					)

             __A       =						self._get_variance(
                 UpperCamelCase_			,						predicted_variance=UpperCamelCase_			,						prev_timestep=UpperCamelCase_			,						)

             if self.variance_type == "fixed_small_log":
                 __A       =						variance
             elif self.variance_type == "learned_range":
                 __A       =						(0.5 * variance).exp()
             else:
                 raise ValueError(
                     F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
                     """ for the UnCLIPScheduler."""					)

             __A       =						variance * variance_noise

         __A       =						pred_prev_sample + variance

         if not return_dict:
             return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase_			,						pred_original_sample=UpperCamelCase_					)





     def 		lowerCAmelCase_  (					self							:      Optional[Any]			,						UpperCamelCase_							:      torch.FloatTensor			,						UpperCamelCase_							:      torch.FloatTensor			,						UpperCamelCase_							:      torch.IntTensor			,						):


         """simple docstring"""



         __A       =						self.alphas_cumprod.to(device=original_samples.device			,						dtype=original_samples.dtype					)
         __A       =						timesteps.to(original_samples.device					)

         __A       =						alphas_cumprod[timesteps] ** 0.5
         __A       =						sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
             __A       =						sqrt_alpha_prod.unsqueeze(-1					)

         __A       =						(1 - alphas_cumprod[timesteps]) ** 0.5
         __A       =						sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
             __A       =						sqrt_one_minus_alpha_prod.unsqueeze(-1					)

         __A       =						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples












import argparse
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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a   :							int						= 16
__a   :							str						= 32



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Accelerator      ,					__lowercase :      int = 1_6     )    ->   Optional[Any]:





    """simple docstring"""

    __A       =						AutoTokenizer.from_pretrained("""bert-base-cased"""     )
    __A       =						load_dataset("""glue"""      ,					"""mrpc"""     )

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

    # Apply the method we just defined to all the examples in all the splits of the dataset
    # starting with the main process first:
    with accelerator.main_process_first():
        __A       =						datasets.map(
            __lowercase      ,					batched=__lowercase      ,					remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,					)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    __A       =						tokenized_datasets.rename_column("""label"""      ,					"""labels"""     )

    def collate_fn(__lowercase :      List[Any]     ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __A       =						1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __A       =						1_6
        elif accelerator.mixed_precision != "no":
            __A       =						8
        else:
            __A       =						None

        return tokenizer.pad(
            __lowercase      ,					padding="""longest"""      ,					max_length=__lowercase      ,					pad_to_multiple_of=__lowercase      ,					return_tensors="""pt"""      ,					)

    # Instantiate dataloaders.
    __A       =						DataLoader(
        tokenized_datasets["""train"""]      ,					shuffle=__lowercase      ,					collate_fn=__lowercase      ,					batch_size=__lowercase     )
    __A       =						DataLoader(
        tokenized_datasets["""validation"""]      ,					shuffle=__lowercase      ,					collate_fn=__lowercase      ,					batch_size=__lowercase     )

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  __a   :							int						= mocked_dataloaders  # noqa: F811



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





    """simple docstring"""

    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,					__lowercase     ) == "1":
        __A       =						2
    # New Code #
    __A       =						int(args.gradient_accumulation_steps     )
    __A       =						int(args.local_sgd_steps     )
    # Initialize accelerator
    __A       =						Accelerator(
        cpu=args.cpu      ,					mixed_precision=args.mixed_precision      ,					gradient_accumulation_steps=__lowercase     )
    if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
        raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""     )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __A       =						config["""lr"""]
    __A       =						int(config["""num_epochs"""]     )
    __A       =						int(config["""seed"""]     )
    __A       =						int(config["""batch_size"""]     )

    __A       =						evaluate.load("""glue"""      ,					"""mrpc"""     )

    set_seed(__lowercase     )
    __A     ,						__A       =						get_dataloaders(__lowercase      ,					__lowercase     )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __A       =						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""      ,					return_dict=__lowercase     )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    __A       =						model.to(accelerator.device     )

    # Instantiate optimizer
    __A       =						AdamW(params=model.parameters()      ,					lr=__lowercase     )

    # Instantiate scheduler
    __A       =						get_linear_schedule_with_warmup(
        optimizer=__lowercase      ,					num_warmup_steps=1_0_0      ,					num_training_steps=(len(__lowercase     ) * num_epochs)      ,					)

    # 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.
    __A     ,						__A     ,						__A     ,						__A     ,						__A       =						accelerator.prepare(
        __lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase     )

    # Now we train the model
    for epoch in range(__lowercase     ):
        model.train()
        with LocalSGD(
            accelerator=__lowercase      ,					model=__lowercase      ,					local_sgd_steps=__lowercase      ,					enabled=local_sgd_steps is not None     ) as local_sgd:
            for step, batch in enumerate(__lowercase     ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device     )
                # New code #
                # We use the new `accumulate` context manager to perform gradient accumulation
                # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                with accelerator.accumulate(__lowercase     ):
                    __A       =						model(**__lowercase     )
                    __A       =						output.loss
                    accelerator.backward(__lowercase     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # LocalSGD-specific line
                    local_sgd.step()

        model.eval()
        for step, batch in enumerate(__lowercase     ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device     )
            with torch.no_grad():
                __A       =						model(**__lowercase     )
            __A       =						outputs.logits.argmax(dim=-1     )
            __A     ,						__A       =						accelerator.gather_for_metrics((predictions, batch["""labels"""])     )
            metric.add_batch(
                predictions=__lowercase      ,					references=__lowercase      ,					)

        __A       =						metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"epoch {epoch}:"      ,					__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

    __A       =						argparse.ArgumentParser(description="""Simple example of training script."""     )
    parser.add_argument(
        """--mixed_precision"""      ,					type=__lowercase      ,					default=__lowercase      ,					choices=["""no""", """fp16""", """bf16""", """fp8"""]      ,					help="""Whether to use mixed precision. Choose"""
        """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
        """and an Nvidia Ampere GPU."""      ,					)
    # New Code #
    parser.add_argument(
        """--gradient_accumulation_steps"""      ,					type=__lowercase      ,					default=1      ,					help="""The number of minibatches to be ran before gradients are accumulated."""      ,					)
    parser.add_argument(
        """--local_sgd_steps"""      ,					type=__lowercase      ,					default=8      ,					help="""Number of local SGD steps or None to disable local SGD"""     )
    parser.add_argument("""--cpu"""      ,					action="""store_true"""      ,					help="""If passed, will train on the CPU."""     )
    __A       =						parser.parse_args()
    __A       =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
    training_function(__lowercase      ,					__lowercase     )


if __name__ == "__main__":
  main()




