



'''simple docstring'''



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=0.9_99  ,   _lowerCAmelCase="cosine"  ,   ):



							"""simple docstring"""



							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_lowerCAmelCase					):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

							elif alpha_transform_type == "exp":

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

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

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





class _UpperCamelCase		(     A      ,       A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    [e.name for e in KarrasDiffusionSchedulers]
				lowerCAmelCase__					    =    2

				@register_to_config
				def __init__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  int = 1_0_0_0  ,       _lowerCAmelCase  :  float = 0.0_0085  ,       _lowerCAmelCase  :  float = 0.012  ,       _lowerCAmelCase  :  str = "linear"  ,       _lowerCAmelCase  :  Optional[Union[np.ndarray, List[float]]] = None  ,       _lowerCAmelCase  :  str = "epsilon"  ,       _lowerCAmelCase  :  str = "linspace"  ,       _lowerCAmelCase  :  int = 0  ,       ):







											'''simple docstring'''

											if trained_betas is not None:
																		__lowercase       	=torch.tensor(_lowerCAmelCase  ,       dtype=torch.floataa)
											elif beta_schedule == "linear":
																		__lowercase       	=torch.linspace(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase  ,       dtype=torch.floataa)
											elif beta_schedule == "scaled_linear":
																		# this schedule is very specific to the latent diffusion model.
																		__lowercase       	=(
																		    torch.linspace(beta_start**0.5  ,       beta_end**0.5  ,       _lowerCAmelCase  ,       dtype=torch.floataa) ** 2
																		)
											elif beta_schedule == "squaredcos_cap_v2":
																		# Glide cosine schedule
																		__lowercase       	=betas_for_alpha_bar(_lowerCAmelCase)
											else:
																		raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}""")

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

											#  set all values
											self.set_timesteps(_lowerCAmelCase  ,       _lowerCAmelCase  ,       _lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  List[str]  ,       _lowerCAmelCase  :  List[Any]  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											if schedule_timesteps is None:
																		__lowercase       	=self.timesteps

											__lowercase       	=(schedule_timesteps == timestep).nonzero()

											# The sigma index that is taken for the **very** first `step`
											# is always the second index (or the last index if there is only 1)
											# This way we can ensure we don't accidentally skip a sigma in
											# case we start in the middle of the denoising schedule (e.g. for image-to-image)
											if len(self._index_counter) == 0:
																		__lowercase       	=1 if len(_lowerCAmelCase) > 1 else 0
											else:
																		__lowercase       	=timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase) else timestep
																		__lowercase       	=self._index_counter[timestep_int]

											return indices[pos].item()

				@property
				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											if self.config.timestep_spacing in ["linspace", "trailing"]:
																		return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  Union[float, torch.FloatTensor]  ,       ):







											'''simple docstring'''

											__lowercase       	=self.index_for_timestep(_lowerCAmelCase)

											if self.state_in_first_order:
																		__lowercase       	=self.sigmas[step_index]
											else:
																		__lowercase       	=self.sigmas_interpol[step_index]

											__lowercase       	=sample / ((sigma**2 + 1) ** 0.5)
											return sample

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  int  ,       _lowerCAmelCase  :  Union[str, torch.device] = None  ,       _lowerCAmelCase  :  Optional[int] = None  ,       ):







											'''simple docstring'''

											__lowercase       	=num_inference_steps

											__lowercase       	=num_train_timesteps or self.config.num_train_timesteps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																		__lowercase       	=np.linspace(0  ,       num_train_timesteps - 1  ,       _lowerCAmelCase  ,       dtype=_lowerCAmelCase)[::-1].copy()
											elif self.config.timestep_spacing == "leading":
																		__lowercase       	=num_train_timesteps // self.num_inference_steps
																		# creates integer timesteps by multiplying by ratio
																		# casting to int to avoid issues when num_inference_step is power of 3
																		__lowercase       	=(np.arange(0  ,       _lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase)
																		timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																		__lowercase       	=num_train_timesteps / self.num_inference_steps
																		# creates integer timesteps by multiplying by ratio
																		# casting to int to avoid issues when num_inference_step is power of 3
																		__lowercase       	=(np.arange(_lowerCAmelCase  ,       0  ,       -step_ratio)).round().copy().astype(_lowerCAmelCase)
																		timesteps -= 1
											else:
																		raise ValueError(
																		    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.""")

											__lowercase       	=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
											__lowercase       	=torch.from_numpy(np.log(_lowerCAmelCase)).to(_lowerCAmelCase)

											__lowercase       	=np.interp(_lowerCAmelCase  ,       np.arange(0  ,       len(_lowerCAmelCase))  ,       _lowerCAmelCase)
											__lowercase       	=np.concatenate([sigmas, [0.0]]).astype(np.floataa)
											__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(device=_lowerCAmelCase)

											# interpolate sigmas
											__lowercase       	=sigmas.log().lerp(sigmas.roll(1).log()  ,       0.5).exp()

											__lowercase       	=torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
											__lowercase       	=torch.cat(
											    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

											if str(_lowerCAmelCase).startswith('mps'):
																		# mps does not support float64
																		__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase  ,       dtype=torch.floataa)
											else:
																		__lowercase       	=torch.from_numpy(_lowerCAmelCase).to(_lowerCAmelCase)

											# interpolate timesteps
											__lowercase       	=self.sigma_to_t(_lowerCAmelCase).to(_lowerCAmelCase  ,       dtype=timesteps.dtype)
											__lowercase       	=torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,       dim=-1).flatten()

											__lowercase       	=torch.cat([timesteps[:1], interleaved_timesteps])

											__lowercase       	=None

											# for exp beta schedules, such as the one for `pipeline_shap_e.py`
											# we need an index counter
											__lowercase       	=defaultdict(_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Dict  ,       _lowerCAmelCase  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=sigma.log()

											# get distribution
											__lowercase       	=log_sigma - self.log_sigmas[:, None]

											# get sigmas range
											__lowercase       	=dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
											__lowercase       	=low_idx + 1

											__lowercase       	=self.log_sigmas[low_idx]
											__lowercase       	=self.log_sigmas[high_idx]

											# interpolate sigmas
											__lowercase       	=(low - log_sigma) / (low - high)
											__lowercase       	=w.clamp(0  ,       1)

											# transform interpolation to time range
											__lowercase       	=(1 - w) * low_idx + w * high_idx
											__lowercase       	=t.view(sigma.shape)
											return t

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											return self.sample is None

				def 				__lowerCamelCase     (							self  :  Optional[Any]  ,       _lowerCAmelCase  :  Union[torch.FloatTensor, np.ndarray]  ,       _lowerCAmelCase  :  Union[float, torch.FloatTensor]  ,       _lowerCAmelCase  :  Union[torch.FloatTensor, np.ndarray]  ,       _lowerCAmelCase  :  bool = True  ,       ):







											'''simple docstring'''

											__lowercase       	=self.index_for_timestep(_lowerCAmelCase)

											# advance index counter by 1
											__lowercase       	=timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase) else timestep
											self._index_counter[timestep_int] += 1

											if self.state_in_first_order:
																		__lowercase       	=self.sigmas[step_index]
																		__lowercase       	=self.sigmas_interpol[step_index + 1]
																		__lowercase       	=self.sigmas[step_index + 1]
											else:
																		# 2nd order / KDPM2's method
																		__lowercase       	=self.sigmas[step_index - 1]
																		__lowercase       	=self.sigmas_interpol[step_index]
																		__lowercase       	=self.sigmas[step_index]

											# currently only gamma=0 is supported. This usually works best anyways.
											# We can support gamma in the future but then need to scale the timestep before
											# passing it to the model which requires a change in API
											__lowercase       	=0
											__lowercase       	=sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											if self.config.prediction_type == "epsilon":
																		__lowercase       	=sigma_hat if self.state_in_first_order else sigma_interpol
																		__lowercase       	=sample - sigma_input * model_output
											elif self.config.prediction_type == "v_prediction":
																		__lowercase       	=sigma_hat if self.state_in_first_order else sigma_interpol
																		__lowercase       	=model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																		    sample / (sigma_input**2 + 1)
																		)
											elif self.config.prediction_type == "sample":
																		raise NotImplementedError('prediction_type not implemented yet: sample')
											else:
																		raise ValueError(
																		    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`""")

											if self.state_in_first_order:
																		# 2. Convert to an ODE derivative for 1st order
																		__lowercase       	=(sample - pred_original_sample) / sigma_hat
																		# 3. delta timestep
																		__lowercase       	=sigma_interpol - sigma_hat

																		# store for 2nd order step
																		__lowercase       	=sample
											else:
																		# DPM-Solver-2
																		# 2. Convert to an ODE derivative for 2nd order
																		__lowercase       	=(sample - pred_original_sample) / sigma_interpol

																		# 3. delta timestep
																		__lowercase       	=sigma_next - sigma_hat

																		__lowercase       	=self.sample
																		__lowercase       	=None

											__lowercase       	=sample + derivative * dt

											if not return_dict:
																		return (prev_sample,)

											return SchedulerOutput(prev_sample=_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       _lowerCAmelCase  :  torch.FloatTensor  ,       ):







											'''simple docstring'''

											__lowercase       	=self.sigmas.to(device=original_samples.device  ,       dtype=original_samples.dtype)
											if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase):
																		# mps does not support float64
																		__lowercase       	=self.timesteps.to(original_samples.device  ,       dtype=torch.floataa)
																		__lowercase       	=timesteps.to(original_samples.device  ,       dtype=torch.floataa)
											else:
																		__lowercase       	=self.timesteps.to(original_samples.device)
																		__lowercase       	=timesteps.to(original_samples.device)

											__lowercase       	=[self.index_for_timestep(_lowerCAmelCase  ,       _lowerCAmelCase) for t in timesteps]

											__lowercase       	=sigmas[step_indices].flatten()
											while len(sigma.shape) < len(original_samples.shape):
																		__lowercase       	=sigma.unsqueeze(-1)

											__lowercase       	=original_samples + noise * sigma
											return noisy_samples

				def __len__(							self  :  Tuple):







											'''simple docstring'''

											return self.config.num_train_timesteps








'''simple docstring'''



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
#
########################################################################


lowerCamelCase	 =       16
lowerCamelCase	 =       32



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = 16					):



							"""simple docstring"""



							__lowercase       	=AutoTokenizer.from_pretrained('bert-base-cased'					)
							__lowercase       	=load_dataset('glue'  ,   'mrpc'					)

							def tokenize_function(_lowerCAmelCase					):
														# max_length=None => use the model max length (it's actually the default)
														__lowercase       	=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
							# starting with the main process first:
							with accelerator.main_process_first():
														__lowercase       	=datasets.map(
														    _lowerCAmelCase  ,   batched=_lowerCAmelCase  ,   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
							__lowercase       	=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.
														__lowercase       	=128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					__lowercase       	=16
														elif accelerator.mixed_precision != "no":
																					__lowercase       	=8
														else:
																					__lowercase       	=None

														return tokenizer.pad(
														    _lowerCAmelCase  ,   padding='longest'  ,   max_length=_lowerCAmelCase  ,   pad_to_multiple_of=_lowerCAmelCase  ,   return_tensors='pt'  ,   )

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

							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

		lowerCamelCase	 =       mocked_dataloaders  # noqa: F811



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,   _lowerCAmelCase					) == "1":
														__lowercase       	=2
							# New Code #
							__lowercase       	=int(args.gradient_accumulation_steps					)
							__lowercase       	=int(args.local_sgd_steps					)
							# Initialize accelerator
							__lowercase       	=Accelerator(
							    cpu=args.cpu  ,   mixed_precision=args.mixed_precision  ,   gradient_accumulation_steps=_lowerCAmelCase					)
							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
							__lowercase       	=config['lr']
							__lowercase       	=int(config['num_epochs']					)
							__lowercase       	=int(config['seed']					)
							__lowercase       	=int(config['batch_size']					)

							__lowercase       	=evaluate.load('glue'  ,   'mrpc'					)

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

							# 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).
							__lowercase       	=model.to(accelerator.device					)

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

							# Instantiate scheduler
							__lowercase       	=get_linear_schedule_with_warmup(
							    optimizer=_lowerCAmelCase  ,   num_warmup_steps=100  ,   num_training_steps=(len(_lowerCAmelCase					) * 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.
							__lowercase	,				__lowercase	,				__lowercase	,				__lowercase	,				__lowercase       	=accelerator.prepare(
							    _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							# Now we train the model
							for epoch in range(_lowerCAmelCase					):
														model.train()
														with LocalSGD(
														    accelerator=_lowerCAmelCase  ,   model=_lowerCAmelCase  ,   local_sgd_steps=_lowerCAmelCase  ,   enabled=local_sgd_steps is not None					) as local_sgd:
																					for step, batch in enumerate(_lowerCAmelCase					):
																												# 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(_lowerCAmelCase					):
																																			__lowercase       	=model(**_lowerCAmelCase					)
																																			__lowercase       	=output.loss
																																			accelerator.backward(_lowerCAmelCase					)
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()
																																			# LocalSGD-specific line
																																			local_sgd.step()

														model.eval()
														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():
																												__lowercase       	=model(**_lowerCAmelCase					)
																					__lowercase       	=outputs.logits.argmax(dim=-1					)
																					__lowercase	,				__lowercase       	=accelerator.gather_for_metrics((predictions, batch['labels'])					)
																					metric.add_batch(
																					    predictions=_lowerCAmelCase  ,   references=_lowerCAmelCase  ,   )

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



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=argparse.ArgumentParser(description='Simple example of training script.'					)
							parser.add_argument(
							    '--mixed_precision'  ,   type=_lowerCAmelCase  ,   default=_lowerCAmelCase  ,   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=_lowerCAmelCase  ,   default=1  ,   help='The number of minibatches to be ran before gradients are accumulated.'  ,   )
							parser.add_argument(
							    '--local_sgd_steps'  ,   type=_lowerCAmelCase  ,   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.'					)
							__lowercase       	=parser.parse_args()
							__lowercase       	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(_lowerCAmelCase  ,   _lowerCAmelCase					)


if __name__ == "__main__":
		main()



