


"""simple docstring"""




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


lowercase_			     =  logging.get_logger(__name__)  # pylint: disable=invalid-name
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, _a							, ):
				super().__init__()

				if hasattr(scheduler.config							, '''steps_offset'''      ) and scheduler.config.steps_offset != 1:
						__a           =			(
						    f'''The configuration file of this scheduler: {scheduler} is outdated.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'''							, _a							, standard_warn=_a      )
						__a           =			dict(scheduler.config      )
						__a           =			1
						__a           =			FrozenDict(_a      )

				if hasattr(scheduler.config							, '''skip_prk_steps'''      ) and scheduler.config.skip_prk_steps is False:
						__a           =			(
						    f'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
						    ''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
						    ''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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('''skip_prk_steps not set'''							, '''1.0.0'''							, _a							, standard_warn=_a      )
						__a           =			dict(scheduler.config      )
						__a           =			True
						__a           =			FrozenDict(_a      )

				if safety_checker is None:
						logger.warning(
						    f'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
						    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
						    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
						    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
						    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
						    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''      )

				self.register_modules(
				    segmentation_model=_a							, segmentation_processor=_a							, vae=_a							, text_encoder=_a							, tokenizer=_a							, unet=_a							, scheduler=_a							, safety_checker=_a							, feature_extractor=_a							, )

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

		def 			__UpperCAmelCase       (  self      ):
				self.enable_attention_slicing(_a      )

		def 			__UpperCAmelCase       (  self      ):
				if is_accelerate_available():
						from accelerate import cpu_offload
				else:
						raise ImportError('''Please install accelerate via `pip install accelerate`'''      )

				__a           =			torch.device('''cuda'''      )

				for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
						if cpu_offloaded_model is not None:
								cpu_offload(_a							, _a      )

		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 			__UpperCAmelCase       (  self      ):
				if self.device != torch.device('''meta'''      ) or not hasattr(self.unet							, '''_hf_hook'''      ):
						return self.device
				for module in self.unet.modules():
						if (
						    hasattr(_a							, '''_hf_hook'''      )
						    and hasattr(module._hf_hook							, '''execution_device'''      )
						    and module._hf_hook.execution_device is not None
						):
								return torch.device(module._hf_hook.execution_device      )
				return self.device





		@torch.no_grad()
		def __call__(  self							, _a							, _a							, _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							, **_a							, ):
				__a           =			self.segmentation_processor(
				    text=[text]							, images=[image]							, padding='''max_length'''							, return_tensors='''pt'''      ).to(self.device      )
				__a           =			self.segmentation_model(**_a      )
				__a           =			torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
				__a           =			self.numpy_to_pil(_a      )[0].resize(image.size      )

				# Run inpainting pipeline with the generated mask
				__a           =			StableDiffusionInpaintPipeline(
				    vae=self.vae							, text_encoder=self.text_encoder							, tokenizer=self.tokenizer							, unet=self.unet							, scheduler=self.scheduler							, safety_checker=self.safety_checker							, feature_extractor=self.feature_extractor							, )
				return inpainting_pipeline(
				    prompt=_a							, image=_a							, mask_image=_a							, height=_a							, width=_a							, num_inference_steps=_a							, guidance_scale=_a							, negative_prompt=_a							, num_images_per_prompt=_a							, eta=_a							, generator=_a							, latents=_a							, output_type=_a							, return_dict=_a							, callback=_a							, callback_steps=_a							, )












"""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, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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_						=				16
A_						=				32


def 					UpperCAmelCase__       (snake_case__							:					Accelerator		,   snake_case__							:					int = 16    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  AutoTokenizer.from_pretrained("""bert-base-cased"""    )
  _snake_case							:		Any								=  load_dataset("""glue"""		,   """mrpc"""    )

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

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

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

  # Instantiate dataloaders.
  _snake_case							:		Optional[int]								=  DataLoader(
      tokenized_datasets["""train"""]		,   shuffle=snake_case__		,   collate_fn=snake_case__		,   batch_size=snake_case__    )
  _snake_case							:		Dict								=  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

     A_						=				mocked_dataloaders  # noqa: F811


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Any    ):
  """simple docstring"""

  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,   snake_case__    ) == "1":
    _snake_case							:		List[Any]								=  2
  # Initialize accelerator
  _snake_case							:		str								=  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							:		Tuple								=  config["""lr"""]
  _snake_case							:		str								=  int(config["""num_epochs"""]    )
  _snake_case							:		Union[str, Any]								=  int(config["""seed"""]    )
  _snake_case							:		Union[str, Any]								=  int(config["""batch_size"""]    )

  _snake_case							:		List[str]								=  evaluate.load("""glue"""		,   """mrpc"""    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=snake_case__    )
  def inner_training_loop(snake_case__							:					Union[str, Any]    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(snake_case__    )

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

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

    # Instantiate scheduler
    _snake_case							:		str								=  get_linear_schedule_with_warmup(
        optimizer=snake_case__		,   num_warmup_steps=1_00		,   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.
    _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		List[str]								=  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    )
        _snake_case							:		int								=  model(**snake_case__    )
        _snake_case							:		str								=  outputs.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():
          _snake_case							:		int								=  model(**snake_case__    )
        _snake_case							:		Optional[Any]								=  outputs.logits.argmax(dim=-1    )
        _snake_case		,					_snake_case							:		Tuple								=  accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
        metric.add_batch(
            predictions=snake_case__		,   references=snake_case__		,   )

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

    # 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 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		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."""		,   )
  parser.add_argument("""--cpu"""		,   action="""store_true"""		,   help="""If passed, will train on the CPU."""    )
  _snake_case							:		Dict								=  parser.parse_args()
  _snake_case							:		int								=  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(snake_case__		,   snake_case__    )


if __name__ == "__main__":
     main()


