






"""simple docstring"""







from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


_UpperCamelCase     			=  logging.get_logger(__name__)  # pylint: disable=invalid-name







class 					lowerCamelCase__					(       snake_case_ ):
    SCREAMING_SNAKE_CASE		=			['safety_checker', 'feature_extractor']






    def __init__( self	,A	,A	,A	,A	,A	,A	,A	,A = True	,):
          super().__init__()
          if hasattr(scheduler.config	,"""steps_offset"""					) and scheduler.config.steps_offset != 1:
                UpperCAmelCase							=       (
                    F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                    F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
                    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
                    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
                    ' file'
                )
                deprecate("""steps_offset!=1"""	,"""1.0.0"""	,lowerCamelCase_	,standard_warn=lowerCamelCase_					)
                UpperCAmelCase							=       dict(scheduler.config					)
                UpperCAmelCase							=       1
                UpperCAmelCase							=       FrozenDict(lowerCamelCase_					)

          self.register_modules(
              vae=lowerCamelCase_	,text_encoder=lowerCamelCase_	,tokenizer=lowerCamelCase_	,unet=lowerCamelCase_	,scheduler=lowerCamelCase_	,safety_checker=lowerCamelCase_	,feature_extractor=lowerCamelCase_	,)
          self.register_to_config(requires_safety_checker=lowerCamelCase_					)






    @property
    def 				_UpperCamelCase							( self					):
          return {k: getattr(self	,lowerCamelCase_					) for k in self.config.keys() if not k.startswith("""_"""					)}






    def 				_UpperCamelCase							( self	,A = "auto"					):



          if slice_size == "auto":
                # half the attention head size is usually a good trade-off between
                # speed and memory
                UpperCAmelCase							=       self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(lowerCamelCase_					)






    def 				_UpperCamelCase							( self					):
          self.enable_attention_slicing(lowerCamelCase_					)






    @torch.no_grad()
    def 				_UpperCamelCase							( self	,A	,A	,A	,A = 0.8	,A = 50	,A = 7.5	,A = None	,A = 1	,A = 0.0	,A = None	,A = "pil"	,A = True	,A = None	,A = 1	,):
          return StableDiffusionInpaintPipelineLegacy(**self.components					)(
              prompt=lowerCamelCase_	,image=lowerCamelCase_	,mask_image=lowerCamelCase_	,strength=lowerCamelCase_	,num_inference_steps=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,negative_prompt=lowerCamelCase_	,num_images_per_prompt=lowerCamelCase_	,eta=lowerCamelCase_	,generator=lowerCamelCase_	,output_type=lowerCamelCase_	,return_dict=lowerCamelCase_	,callback=lowerCamelCase_	,)






    @torch.no_grad()
    def 				_UpperCamelCase							( self	,A	,A	,A = 0.8	,A = 50	,A = 7.5	,A = None	,A = 1	,A = 0.0	,A = None	,A = "pil"	,A = True	,A = None	,A = 1	,**A	,):
          return StableDiffusionImgaImgPipeline(**self.components					)(
              prompt=lowerCamelCase_	,image=lowerCamelCase_	,strength=lowerCamelCase_	,num_inference_steps=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,negative_prompt=lowerCamelCase_	,num_images_per_prompt=lowerCamelCase_	,eta=lowerCamelCase_	,generator=lowerCamelCase_	,output_type=lowerCamelCase_	,return_dict=lowerCamelCase_	,callback=lowerCamelCase_	,callback_steps=lowerCamelCase_	,)






    @torch.no_grad()
    def 				_UpperCamelCase							( self	,A	,A = 512	,A = 512	,A = 50	,A = 7.5	,A = None	,A = 1	,A = 0.0	,A = None	,A = None	,A = "pil"	,A = True	,A = None	,A = 1	,):
          return StableDiffusionPipeline(**self.components					)(
              prompt=lowerCamelCase_	,height=lowerCamelCase_	,width=lowerCamelCase_	,num_inference_steps=lowerCamelCase_	,guidance_scale=lowerCamelCase_	,negative_prompt=lowerCamelCase_	,num_images_per_prompt=lowerCamelCase_	,eta=lowerCamelCase_	,generator=lowerCamelCase_	,latents=lowerCamelCase_	,output_type=lowerCamelCase_	,return_dict=lowerCamelCase_	,callback=lowerCamelCase_	,callback_steps=lowerCamelCase_	,)











"""simple docstring"""
import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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_			: Tuple				=			32






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase = 16				):
 lowerCamelCase__     :			Union[str, Any]     				=						AutoTokenizer.from_pretrained('bert-base-cased'				)
 lowerCamelCase__     :			Optional[int]     				=						load_dataset('glue'			,   'mrpc'				)

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

 def collate_fn(_lowerCamelCase				):
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   lowerCamelCase__     :			Any     				=						16
  elif accelerator.mixed_precision != "no":
   lowerCamelCase__     :			Any     				=						8
  else:
   lowerCamelCase__     :			Dict     				=						None

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

 # Instantiate dataloaders.
 lowerCamelCase__     :			List[str]     				=						DataLoader(
     tokenized_datasets['train']			,   shuffle=_lowerCamelCase			,   collate_fn=_lowerCamelCase			,   batch_size=_lowerCamelCase				)
 lowerCamelCase__     :			Tuple     				=						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

      A_			: List[str]				=			mocked_dataloaders  # noqa: F811






def        lowerCamelCase_  (						_lowerCamelCase			,   _lowerCamelCase				):
 # For testing only
 if os.environ.get('TESTING_MOCKED_DATALOADERS'			,   _lowerCamelCase				) == "1":
  lowerCamelCase__     :			List[str]     				=						2
 # Initialize accelerator
 lowerCamelCase__     :			List[str]     				=						Accelerator(cpu=args.cpu			,   mixed_precision=args.mixed_precision				)
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowerCamelCase__     :			Any     				=						config['lr']
 lowerCamelCase__     :			List[str]     				=						int(config['num_epochs']				)
 lowerCamelCase__     :			Optional[Any]     				=						int(config['seed']				)
 lowerCamelCase__     :			int     				=						int(config['batch_size']				)

 lowerCamelCase__     :			Union[str, Any]     				=						evaluate.load('glue'			,   'mrpc'				)

 # New Code #
 # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
 # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
 # half each time. From this, we can calculate the number of gradient accumulation steps needed
 # and modify the Accelerator object as a result
 @find_executable_batch_size(starting_batch_size=int(_lowerCamelCase				)				)
 def inner_training_loop(_lowerCamelCase				):
  # Since we need to modify the outside accelerator object, we need to bring it
  # to the local scope
  nonlocal accelerator

  # We can calculate the number of gradient accumulation steps based on the current
  # batch size vs the starting batch size
  lowerCamelCase__     :			List[str]     				=						observed_batch_size // batch_size

  # And then set it in the Accelerator directly:
  lowerCamelCase__     :			Any     				=						num_gradient_accumulation_steps

  # Next we need to free all of the stored model references in the Accelerator each time
  accelerator.free_memory()

  # And set the seed so our results are reproducable each reset
  set_seed(_lowerCamelCase				)

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  lowerCamelCase__     :			Dict     				=						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).
  lowerCamelCase__     :			Optional[Any]     				=						model.to(accelerator.device				)

  # Instantiate optimizer
  lowerCamelCase__     :			int     				=						AdamW(params=model.parameters()			,   lr=_lowerCamelCase				)
  lowerCamelCase__				,     lowerCamelCase__     :			List[Any]     				=						get_dataloaders(_lowerCamelCase			,   _lowerCamelCase				)

  # Instantiate scheduler
  lowerCamelCase__     :			int     				=						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.
  lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__     :			Optional[int]     				=						accelerator.prepare(
      _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase			,   _lowerCamelCase				)

  # Now we train the model
  for epoch in range(_lowerCamelCase				):
   model.train()
   for step, batch in enumerate(_lowerCamelCase				):
    # And perform gradient accumulation
    with accelerator.accumulate(_lowerCamelCase				):
     # We could avoid this line since we set the accelerator with `device_placement=True`.
     batch.to(accelerator.device				)
     lowerCamelCase__     :			Tuple     				=						model(**_lowerCamelCase				)
     lowerCamelCase__     :			Optional[Any]     				=						outputs.loss
     accelerator.backward(_lowerCamelCase				)
     optimizer.step()
     lr_scheduler.step()
     optimizer.zero_grad()

   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():
     lowerCamelCase__     :			int     				=						model(**_lowerCamelCase				)
    lowerCamelCase__     :			str     				=						outputs.logits.argmax(dim=-1				)
    lowerCamelCase__				,     lowerCamelCase__     :			Dict     				=						accelerator.gather_for_metrics((predictions, batch['labels'])				)
    metric.add_batch(
        predictions=_lowerCamelCase			,   references=_lowerCamelCase			,   )

   lowerCamelCase__     :			Dict     				=						metric.compute()
   # Use accelerator.print to print only on the main process.
   accelerator.print(f'''epoch {epoch}:'''			,   _lowerCamelCase				)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
 inner_training_loop()






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			Any     				=						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.'				)
 lowerCamelCase__     :			List[Any]     				=						parser.parse_args()
 # New Code #
 # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
 lowerCamelCase__     :			Tuple     				=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 256}
 training_function(_lowerCamelCase			,   _lowerCamelCase				)


if __name__ == "__main__":
      main()

