




'''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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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  :				Optional[Any]									=  32


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase = 16	)		->					str:

					'''simple docstring'''

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

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

					def collate_fn(__UpperCAmelCase	):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										snake_case_									=				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":
															snake_case_									=				16
										elif accelerator.mixed_precision != "no":
															snake_case_									=				8
										else:
															snake_case_									=				None

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

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

					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  :				Dict									=  mocked_dataloaders  # noqa: F811


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

					if os.environ.get('''TESTING_MOCKED_DATALOADERS''',      __UpperCAmelCase	) == "1":
										snake_case_									=				2
					# Initialize accelerator
					snake_case_									=				Accelerator(cpu=args.cpu,      mixed_precision=args.mixed_precision	)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					snake_case_									=				config['''lr''']
					snake_case_									=				int(config['''num_epochs''']	)
					snake_case_									=				int(config['''seed''']	)
					snake_case_									=				int(config['''batch_size''']	)

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

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

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

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

					# Instantiate optimizer
					snake_case_									=				AdamW(params=model.parameters(),      lr=__UpperCAmelCase	)

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

					# Now we train the model
					for epoch in range(__UpperCAmelCase	):
										model.train()
										for step, batch in enumerate(__UpperCAmelCase	):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device	)
															snake_case_									=				model(**__UpperCAmelCase	)
															snake_case_									=				outputs.loss
															snake_case_									=				loss / gradient_accumulation_steps
															accelerator.backward(__UpperCAmelCase	)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										snake_case_									=				0
										for step, batch in enumerate(__UpperCAmelCase	):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device	)
															with torch.no_grad():
																				snake_case_									=				model(**__UpperCAmelCase	)
															snake_case_									=				outputs.logits.argmax(dim=-1	)
															snake_case_			,snake_case_									=				accelerator.gather((predictions, batch['''labels'''])	)
															# New Code #
															# First we check if it's a distributed system
															if accelerator.use_distributed:
																				# Then see if we're on the last batch of our eval dataloader
																				if step == len(__UpperCAmelCase	) - 1:
																									# Last batch needs to be truncated on distributed systems as it contains additional samples
																									snake_case_									=				predictions[: len(eval_dataloader.dataset	) - samples_seen]
																									snake_case_									=				references[: len(eval_dataloader.dataset	) - samples_seen]
																				else:
																									# Otherwise we add the number of samples seen
																									samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
															metric.add_batch(
															    predictions=__UpperCAmelCase,      references=__UpperCAmelCase,      )

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


def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				argparse.ArgumentParser(description='''Simple example of training script.'''	)
					parser.add_argument(
					    '''--mixed_precision''',      type=__UpperCAmelCase,      default=__UpperCAmelCase,      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.'''	)
					snake_case_									=				parser.parse_args()
					snake_case_									=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__UpperCAmelCase,      __UpperCAmelCase	)


if __name__ == "__main__":
							main()








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 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Tuple=0.999 ,     UpperCamelCase__			:       Any="cosine" ,     )->List[str]:
       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

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




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__				,					UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	[e.name for e in KarrasDiffusionSchedulers]
 __SCREAMING_SNAKE_CASE							       =	2



 @register_to_config
 def __init__(							self,__lowerCamelCase = 1000,__lowerCamelCase = 0.00085,__lowerCamelCase = 0.012,__lowerCamelCase = "linear",__lowerCamelCase = None,__lowerCamelCase = "epsilon",__lowerCamelCase = False,__lowerCamelCase = False,__lowerCamelCase = 1.0,__lowerCamelCase = "linspace",__lowerCamelCase = 0,):
        if trained_betas is not None:
               A__											=						torch.tensor(__lowerCamelCase,dtype=torch.floataa )
        elif beta_schedule == "linear":
               A__											=						torch.linspace(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,dtype=torch.floataa )
        elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               A__											=						(
                   torch.linspace(beta_start**0.5,beta_end**0.5,__lowerCamelCase,dtype=torch.floataa ) ** 2
               )
        elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               A__											=						betas_for_alpha_bar(__lowerCamelCase,alpha_transform_type='''cosine''' )
        elif beta_schedule == "exp":
               A__											=						betas_for_alpha_bar(__lowerCamelCase,alpha_transform_type='''exp''' )
        else:
               raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )

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

        #  set all values
        self.set_timesteps(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase )
        A__											=						use_karras_sigmas



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase=None ):
        if schedule_timesteps is None:
               A__											=						self.timesteps

        A__											=						(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:
               A__											=						1 if len(__lowerCamelCase ) > 1 else 0
        else:
               A__											=						timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
               A__											=						self._index_counter[timestep_int]

        return indices[pos].item()



 @property
 def 				UpperCamelCase			(							self ):
        # standard deviation of the initial noise distribution
        if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

        A__											=						self.sigmas[step_index]
        A__											=						sample / ((sigma**2 + 1) ** 0.5)
        return sample



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,):
        A__											=						num_inference_steps

        A__											=						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":
               A__											=						np.linspace(0,num_train_timesteps - 1,__lowerCamelCase,dtype=__lowerCamelCase )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
               A__											=						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
               A__											=						(np.arange(0,__lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase )
               timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
               A__											=						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
               A__											=						(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'." )

        A__											=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
        A__											=						np.log(__lowerCamelCase )
        A__											=						np.interp(__lowerCamelCase,np.arange(0,len(__lowerCamelCase ) ),__lowerCamelCase )

        if self.config.use_karras_sigmas:
               A__											=						self._convert_to_karras(in_sigmas=__lowerCamelCase,num_inference_steps=self.num_inference_steps )
               A__											=						np.array([self._sigma_to_t(__lowerCamelCase,__lowerCamelCase ) for sigma in sigmas] )

        A__											=						np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
        A__											=						torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase )
        A__											=						torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

        A__											=						torch.from_numpy(__lowerCamelCase )
        A__											=						torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

        if str(__lowerCamelCase ).startswith('''mps''' ):
               # mps does not support float64
               A__											=						timesteps.to(__lowerCamelCase,dtype=torch.floataa )
        else:
               A__											=						timesteps.to(device=__lowerCamelCase )

        # empty dt and derivative
        A__											=						None
        A__											=						None

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        # get log sigma
        A__											=						np.log(__lowerCamelCase )

        # get distribution
        A__											=						log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        A__											=						np.cumsum((dists >= 0),axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
        A__											=						low_idx + 1

        A__											=						log_sigmas[low_idx]
        A__											=						log_sigmas[high_idx]

        # interpolate sigmas
        A__											=						(low - log_sigma) / (low - high)
        A__											=						np.clip(__lowerCamelCase,0,1 )

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



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						in_sigmas[-1].item()
        A__											=						in_sigmas[0].item()

        A__											=						7.0  # 7.0 is the value used in the paper
        A__											=						np.linspace(0,1,__lowerCamelCase )
        A__											=						sigma_min ** (1 / rho)
        A__											=						sigma_max ** (1 / rho)
        A__											=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas



 @property
 def 				UpperCamelCase			(							self ):
        return self.dt is None



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = True,):
        A__											=						self.index_for_timestep(__lowerCamelCase )

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

        if self.state_in_first_order:
               A__											=						self.sigmas[step_index]
               A__											=						self.sigmas[step_index + 1]
        else:
               # 2nd order / Heun's method
               A__											=						self.sigmas[step_index - 1]
               A__											=						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
        A__											=						0
        A__											=						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":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
               A__											=						sigma_hat if self.state_in_first_order else sigma_next
               A__											=						model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                   sample / (sigma_input**2 + 1)
               )
        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 `v_prediction`" )

        if self.config.clip_sample:
               A__											=						pred_original_sample.clamp(
                   -self.config.clip_sample_range,self.config.clip_sample_range )

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

               # store for 2nd order step
               A__											=						derivative
               A__											=						dt
               A__											=						sample
        else:
               # 2. 2nd order / Heun's method
               A__											=						(sample - pred_original_sample) / sigma_next
               A__											=						(self.prev_derivative + derivative) / 2

               # 3. take prev timestep & sample
               A__											=						self.dt
               A__											=						self.sample

               # free dt and derivative
               # Note, this puts the scheduler in "first order mode"
               A__											=						None
               A__											=						None
               A__											=						None

        A__											=						sample + derivative * dt

        if not return_dict:
               return (prev_sample,)

        return SchedulerOutput(prev_sample=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,):
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        A__											=						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
               A__											=						self.timesteps.to(original_samples.device,dtype=torch.floataa )
               A__											=						timesteps.to(original_samples.device,dtype=torch.floataa )
        else:
               A__											=						self.timesteps.to(original_samples.device )
               A__											=						timesteps.to(original_samples.device )

        A__											=						[self.index_for_timestep(__lowerCamelCase,__lowerCamelCase ) for t in timesteps]

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

        A__											=						original_samples + noise * sigma
        return noisy_samples




 def __len__(							self ):
        return self.config.num_train_timesteps





