



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


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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       					=		16
__A       					=		32


def  __a  (					lowerCAmelCase_     :      Optional[Any]    ,lowerCAmelCase_     :      Optional[Any]       )   ->				int:






						'''simple docstring'''



						if args.with_tracking:
												UpperCAmelCase_=     Accelerator(
												    cpu=args.cpu    ,mixed_precision=args.mixed_precision    ,log_with="""all"""    ,project_dir=args.project_dir       )
						else:
												UpperCAmelCase_=     Accelerator(cpu=args.cpu    ,mixed_precision=args.mixed_precision       )

						if hasattr(args.checkpointing_steps    ,"""isdigit"""       ):
												if args.checkpointing_steps == "epoch":
																		UpperCAmelCase_=     args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		UpperCAmelCase_=     int(args.checkpointing_steps       )
												else:
																		raise ValueError(
																		    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""       )
						else:
												UpperCAmelCase_=     None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_=     config["""lr"""]
						UpperCAmelCase_=     int(config["""num_epochs"""]       )
						UpperCAmelCase_=     int(config["""seed"""]       )
						UpperCAmelCase_=     int(config["""batch_size"""]       )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												UpperCAmelCase_=     os.path.split(lowerCAmelCase_       )[-1].split("""."""       )[0]
												accelerator.init_trackers(lowerCAmelCase_    ,lowerCAmelCase_       )

						UpperCAmelCase_=     AutoTokenizer.from_pretrained("""bert-base-cased"""       )
						UpperCAmelCase_=     load_dataset("""glue"""    ,"""mrpc"""       )
						UpperCAmelCase_=     evaluate.load("""glue"""    ,"""mrpc"""       )

						def tokenize_function(lowerCAmelCase_     :      Union[str, Any]       ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_=     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():
												UpperCAmelCase_=     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
						UpperCAmelCase_=     tokenized_datasets.rename_column("""label"""    ,"""labels"""       )

						# If the batch size is too big we use gradient accumulation
						UpperCAmelCase_=     1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												UpperCAmelCase_=     batch_size // MAX_GPU_BATCH_SIZE
												UpperCAmelCase_=     MAX_GPU_BATCH_SIZE

						def collate_fn(lowerCAmelCase_     :      str       ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												UpperCAmelCase_=     1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		UpperCAmelCase_=     16
												elif accelerator.mixed_precision != "no":
																		UpperCAmelCase_=     8
												else:
																		UpperCAmelCase_=     None

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

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

						set_seed(lowerCAmelCase_       )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_=     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).
						UpperCAmelCase_=     model.to(accelerator.device       )

						# Instantiate optimizer
						UpperCAmelCase_=     AdamW(params=model.parameters()    ,lr=lowerCAmelCase_       )

						# Instantiate scheduler
						UpperCAmelCase_=     get_linear_schedule_with_warmup(
						    optimizer=lowerCAmelCase_    ,num_warmup_steps=1_00    ,num_training_steps=(len(lowerCAmelCase_       ) * num_epochs) // gradient_accumulation_steps    ,)

						# 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_=     accelerator.prepare(
						    lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       )

						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase_=     0
						# We also need to keep track of the stating epoch so files are named properly
						UpperCAmelCase_=     0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""       )
																		accelerator.load_state(args.resume_from_checkpoint       )
																		UpperCAmelCase_=     os.path.basename(args.resume_from_checkpoint       )
												else:
																		# Get the most recent checkpoint
																		UpperCAmelCase_=     [f.name for f in os.scandir(os.getcwd()       ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime       )
																		UpperCAmelCase_=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												UpperCAmelCase_=     os.path.splitext(lowerCAmelCase_       )[0]

												if "epoch" in training_difference:
																		UpperCAmelCase_=     int(training_difference.replace("""epoch_"""    ,""""""       )       ) + 1
																		UpperCAmelCase_=     None
												else:
																		UpperCAmelCase_=     int(training_difference.replace("""step_"""    ,""""""       )       )
																		UpperCAmelCase_=     resume_step // len(lowerCAmelCase_       )
																		resume_step -= starting_epoch * len(lowerCAmelCase_       )

    # Now we train the model
						for epoch in range(lowerCAmelCase_    ,lowerCAmelCase_       ):
												model.train()
												if args.with_tracking:
																		UpperCAmelCase_=     0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		UpperCAmelCase_=     accelerator.skip_first_batches(lowerCAmelCase_    ,lowerCAmelCase_       )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		UpperCAmelCase_=     train_dataloader
												for step, batch in enumerate(lowerCAmelCase_       ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device       )
																		UpperCAmelCase_=     model(**lowerCAmelCase_       )
																		UpperCAmelCase_=     outputs.loss
																		UpperCAmelCase_=     loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(lowerCAmelCase_       )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(lowerCAmelCase_    ,lowerCAmelCase_       ):
																								UpperCAmelCase_=     F"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				UpperCAmelCase_=     os.path.join(args.output_dir    ,lowerCAmelCase_       )
																														accelerator.save_state(lowerCAmelCase_       )

												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():
																								UpperCAmelCase_=     model(**lowerCAmelCase_       )
																		UpperCAmelCase_=     outputs.logits.argmax(dim=-1       )
																		UpperCAmelCase_,     UpperCAmelCase_=     accelerator.gather_for_metrics((predictions, batch["""labels"""])       )
																		metric.add_batch(
																		    predictions=lowerCAmelCase_    ,references=lowerCAmelCase_    ,)

												UpperCAmelCase_=     metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""    ,lowerCAmelCase_       )
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        """accuracy""": eval_metric["""accuracy"""],
																		        """f1""": eval_metric["""f1"""],
																		        """train_loss""": total_loss.item() / len(lowerCAmelCase_       ),
																		        """epoch""": epoch,
																		    }    ,step=lowerCAmelCase_    ,)

												if checkpointing_steps == "epoch":
																		UpperCAmelCase_=     F"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								UpperCAmelCase_=     os.path.join(args.output_dir    ,lowerCAmelCase_       )
																		accelerator.save_state(lowerCAmelCase_       )

						if args.with_tracking:
												accelerator.end_training()


def  __a  (					)   ->				Tuple:






						'''simple docstring'''



						UpperCAmelCase_=     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."""    ,)
						parser.add_argument("""--cpu"""    ,action="""store_true"""    ,help="""If passed, will train on the CPU."""       )
						parser.add_argument(
						    """--checkpointing_steps"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""    ,)
						parser.add_argument(
						    """--resume_from_checkpoint"""    ,type=lowerCAmelCase_    ,default=lowerCAmelCase_    ,help="""If the training should continue from a checkpoint folder."""    ,)
						parser.add_argument(
						    """--with_tracking"""    ,action="""store_true"""    ,help="""Whether to load in all available experiment trackers from the environment and use them for logging."""    ,)
						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(
						    """--project_dir"""    ,type=lowerCAmelCase_    ,default="""logs"""    ,help="""Location on where to store experiment tracking logs` and relevent project information"""    ,)
						UpperCAmelCase_=     parser.parse_args()
						UpperCAmelCase_=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
						training_function(lowerCAmelCase_    ,lowerCAmelCase_       )


if __name__ == "__main__":
	main()









# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 	lowercase		:






	"""simple docstring"""


	a__   :      CommonSchedulerState
	a__   :      jnp.ndarray

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




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		Tuple		,						__UpperCAmelCase			:		CommonSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				int:
							return cls(
							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)






@dataclass
class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      DDIMSchedulerState






class 	lowercase		(				snake_case__				,					snake_case__):






	"""simple docstring"""


	a__   :      Dict	     =							[e.name for e in FlaxKarrasDiffusionSchedulers]

	a__   :      jnp.dtype




	@property
	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
							return True




	@register_to_config
	def __init__(			self			:		List[Any]		,						__UpperCAmelCase			:		int = 1_000		,						__UpperCAmelCase			:		float = 0.0_001		,						__UpperCAmelCase			:		float = 0.02		,						__UpperCAmelCase			:		str = "linear"		,						__UpperCAmelCase			:		Optional[jnp.ndarray] = None		,						__UpperCAmelCase			:		bool = True		,						__UpperCAmelCase			:		int = 0		,						__UpperCAmelCase			:		str = "epsilon"		,						__UpperCAmelCase			:		jnp.dtype = jnp.floataa		,						)    ->				Union[str, Any]:
							UpperCAmelCase_=     dtype




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict		,						__UpperCAmelCase			:		Optional[CommonSchedulerState] = None  )    ->				DDIMSchedulerState:
							if common is None:
													UpperCAmelCase_=     CommonSchedulerState.create(self  )

							# At every step in ddim, we are looking into the previous alphas_cumprod
							# For the final step, there is no previous alphas_cumprod because we are already at 0
							# `set_alpha_to_one` decides whether we set this parameter simply to one or
							# whether we use the final alpha of the "non-previous" one.
							UpperCAmelCase_=     (
							    jnp.array(1.0		,						dtype=self.dtype  ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
							)

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

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

							return DDIMSchedulerState.create(
							    common=__UpperCAmelCase		,						final_alpha_cumprod=__UpperCAmelCase		,						init_noise_sigma=__UpperCAmelCase		,						timesteps=__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		Optional[int] = None  )    ->				jnp.ndarray:
							return sample




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		Tuple = ()  )    ->				DDIMSchedulerState:
							UpperCAmelCase_=     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_=     (jnp.arange(0		,						__UpperCAmelCase  ) * step_ratio).round()[::-1] + self.config.steps_offset

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		Optional[int]		,						__UpperCAmelCase			:		Tuple  )    ->				int:
							UpperCAmelCase_=     state.common.alphas_cumprod[timestep]
							UpperCAmelCase_=     jnp.where(
							    prev_timestep >= 0		,						state.common.alphas_cumprod[prev_timestep]		,						state.final_alpha_cumprod  )
							UpperCAmelCase_=     1 - alpha_prod_t
							UpperCAmelCase_=     1 - alpha_prod_t_prev

							UpperCAmelCase_=     (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		int		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		bool = True		,						)    ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
							if state.num_inference_steps is None:
													raise ValueError(
													    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""  )

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							UpperCAmelCase_=     timestep - self.config.num_train_timesteps // state.num_inference_steps

							UpperCAmelCase_=     state.common.alphas_cumprod
							UpperCAmelCase_=     state.final_alpha_cumprod

							# 2. compute alphas, betas
							UpperCAmelCase_=     alphas_cumprod[timestep]
							UpperCAmelCase_=     jnp.where(prev_timestep >= 0		,						alphas_cumprod[prev_timestep]		,						__UpperCAmelCase  )

							UpperCAmelCase_=     1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							if self.config.prediction_type == "epsilon":
													UpperCAmelCase_=     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
													UpperCAmelCase_=     model_output
							elif self.config.prediction_type == "sample":
													UpperCAmelCase_=     model_output
													UpperCAmelCase_=     (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
													UpperCAmelCase_=     (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
													UpperCAmelCase_=     (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
													raise ValueError(
													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
													    """ `v_prediction`"""  )

							# 4. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							UpperCAmelCase_=     self._get_variance(__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )
							UpperCAmelCase_=     eta * variance ** 0.5

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase_=     (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							UpperCAmelCase_=     alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
													return (prev_sample, state)

							return FlaxDDIMSchedulerOutput(prev_sample=__UpperCAmelCase		,						state=__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return add_noise_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		DDIMSchedulerState		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						__UpperCAmelCase			:		jnp.ndarray		,						)    ->				jnp.ndarray:
							return get_velocity_common(state.common		,						__UpperCAmelCase		,						__UpperCAmelCase		,						__UpperCAmelCase  )




	def __len__(			self			:		Union[str, Any]  )    ->				Optional[int]:
							return self.config.num_train_timesteps




