






"""simple docstring"""
# 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   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	_A							:					CommonSchedulerState

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





	@classmethod
	def  lowerCamelCase(cls  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		return cls(common=lowerCAmelCase_  ,  init_noise_sigma=lowerCAmelCase_  ,  timesteps=lowerCAmelCase_				)






@dataclass
class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					DDPMSchedulerState






class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					List[Any] 			=					[e.name for e in FlaxKarrasDiffusionSchedulers]

	_A							:					jnp.dtype





	@property
	def  lowerCamelCase(self				):
		return True





	@register_to_config
	def __init__(self  ,  lowerCAmelCase_ = 1000  ,  lowerCAmelCase_ = 0.0001  ,  lowerCAmelCase_ = 0.02  ,  lowerCAmelCase_ = "linear"  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = "fixed_small"  ,  lowerCAmelCase_ = True  ,  lowerCAmelCase_ = "epsilon"  ,  lowerCAmelCase_ = jnp.floataa  ,  ):
		A_		:    int						     = dtype





	def  lowerCamelCase(self  ,  lowerCAmelCase_ = None				):
		if common is None:
			A_		:    List[str]						     = CommonSchedulerState.create(self				)

		# standard deviation of the initial noise distribution
		A_		:    Optional[int]						     = jnp.array(1.0  ,  dtype=self.dtype				)

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

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





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None				):
		return sample





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = ()				):
		A_		:    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
		A_		:    Union[str, Any]						     = (jnp.arange(0  ,  lowerCAmelCase_				) * step_ratio).round()[::-1]

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





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None				):
		A_		:    Optional[Any]						     = state.common.alphas_cumprod[t]
		A_		:    Any						     = 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
		A_		:    Tuple						     = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

		if variance_type is None:
			A_		:    Any						     = self.config.variance_type

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

		return variance





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_ = None  ,  lowerCAmelCase_ = True  ,  ):
		A_		:    Union[str, Any]						     = timestep

		if key is None:
			A_		:    int						     = jax.random.PRNGKey(0				)

		if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
			A_     ,  A_		:    List[Any]						     = jnp.split(lowerCAmelCase_  ,  sample.shape[1]  ,  axis=1				)
		else:
			A_		:    Any						     = None

		# 1. compute alphas, betas
		A_		:    List[str]						     = state.common.alphas_cumprod[t]
		A_		:    Optional[int]						     = jnp.where(t > 0  ,  state.common.alphas_cumprod[t - 1]  ,  jnp.array(1.0  ,  dtype=self.dtype				)				)
		A_		:    List[Any]						     = 1 - alpha_prod_t
		A_		:    Any						     = 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":
			A_		:    str						     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
		elif self.config.prediction_type == "sample":
			A_		:    Dict						     = model_output
		elif self.config.prediction_type == "v_prediction":
			A_		:    Optional[int]						     = (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:
			A_		:    Optional[Any]						     = jnp.clip(lowerCAmelCase_  ,  -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
		A_		:    int						     = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
		A_		:    Dict						     = 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
		A_		:    Any						     = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

		# 6. Add noise
		def random_variance():
			A_		:    int						     = jax.random.split(lowerCAmelCase_  ,  num=1				)
			A_		:    int						     = jax.random.normal(lowerCAmelCase_  ,  shape=model_output.shape  ,  dtype=self.dtype				)
			return (self._get_variance(lowerCAmelCase_  ,  lowerCAmelCase_  ,  predicted_variance=lowerCAmelCase_				) ** 0.5) * noise

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

		A_		:    Tuple						     = pred_prev_sample + variance

		if not return_dict:
			return (pred_prev_sample, state)

		return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase_  ,  state=lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  ):
		return add_noise_common(state.common  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  ):
		return get_velocity_common(state.common  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				)





	def __len__(self				):
		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


########################################################################
# This is a fully working simple example to use Accelerate
# and perform 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       __UpperCamelCase						(		snake_case__ , snake_case__ = 16       ):
	A_		:    List[str]						     = AutoTokenizer.from_pretrained("""bert-base-cased"""       )
	A_		:    Optional[Any]						     = load_dataset("""glue""" , """mrpc"""       )

	def tokenize_function(snake_case__       ):
		# max_length=None => use the model max length (it's actually the default)
		A_		:    Tuple						     = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=snake_case__ , max_length=snake_case__       )
		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_		:    str						     = datasets.map(
		    snake_case__ , batched=snake_case__ , 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_		:    int						     = tokenized_datasets.rename_column("""label""" , """labels"""       )

	def collate_fn(snake_case__       ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A_		:    Optional[Any]						     = 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":
			A_		:    Tuple						     = 16
		elif accelerator.mixed_precision != "no":
			A_		:    List[Any]						     = 8
		else:
			A_		:    List[Any]						     = None

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

	# Instantiate dataloaders.
	A_		:    Optional[Any]						     = DataLoader(
	    tokenized_datasets["""train"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )
	A_		:    List[str]						     = DataLoader(
	    tokenized_datasets["""validation"""] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__       )

	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       __UpperCamelCase						(		snake_case__ , snake_case__       ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , snake_case__       ) == "1":
		A_		:    List[Any]						     = 2
	# New Code #
	A_		:    Tuple						     = int(args.gradient_accumulation_steps       )
	# Initialize accelerator
	A_		:    Any						     = Accelerator(
	    cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=snake_case__       )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""       )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A_		:    Union[str, Any]						     = config["""lr"""]
	A_		:    Tuple						     = int(config["""num_epochs"""]       )
	A_		:    List[Any]						     = int(config["""seed"""]       )
	A_		:    Dict						     = int(config["""batch_size"""]       )

	A_		:    Tuple						     = evaluate.load("""glue""" , """mrpc"""       )

	set_seed(snake_case__       )
	A_     ,  A_		:    List[Any]						     = get_dataloaders(snake_case__ , snake_case__       )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_		:    Dict						     = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=snake_case__       )

	# 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_		:    Union[str, Any]						     = model.to(accelerator.device       )

	# Instantiate optimizer
	A_		:    Optional[int]						     = AdamW(params=model.parameters() , lr=snake_case__       )

	# Instantiate scheduler
	A_		:    Union[str, Any]						     = get_linear_schedule_with_warmup(
	    optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__       ) * 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_		:    Optional[int]						     = accelerator.prepare(
	    snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__       )

	# Now we train the model
	for epoch in range(snake_case__       ):
		model.train()
		for step, batch in enumerate(snake_case__       ):
			# 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(snake_case__       ):
				A_		:    Optional[int]						     = model(**snake_case__       )
				A_		:    Tuple						     = output.loss
				accelerator.backward(snake_case__       )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		A_		:    str						     = metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"""epoch {epoch}:""" , snake_case__       )






def       __UpperCamelCase						(		):
	A_		:    List[Any]						     = argparse.ArgumentParser(description="""Simple example of training script."""       )
	parser.add_argument(
	    """--mixed_precision""" , type=snake_case__ , default=snake_case__ , 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=snake_case__ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
	parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU."""       )
	A_		:    int						     = parser.parse_args()
	A_		:    Optional[Any]						     = {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(snake_case__ , snake_case__       )


if __name__ == "__main__":
							main()

