






'''simple docstring'''

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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






class        a_		(    lowerCamelCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	)						->   str:



        """simple docstring"""






        super().__init__()
        self.register_modules(
            vae=_SCREAMING_SNAKE_CASE    ,	text_encoder=_SCREAMING_SNAKE_CASE    ,	tokenizer=_SCREAMING_SNAKE_CASE    ,	unet=_SCREAMING_SNAKE_CASE    ,	scheduler=_SCREAMING_SNAKE_CASE    ,	safety_checker=_SCREAMING_SNAKE_CASE    ,	feature_extractor=_SCREAMING_SNAKE_CASE    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE = "auto"   )						->   int:



        """simple docstring"""






        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(_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        self.enable_attention_slicing(_SCREAMING_SNAKE_CASE   )


  @torch.no_grad()
  def __call__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 512    ,	_SCREAMING_SNAKE_CASE = 50    ,	_SCREAMING_SNAKE_CASE = 7.5    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = 0.0    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "pil"    ,	_SCREAMING_SNAKE_CASE = True    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = 1    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   str:



        """simple docstring"""






        if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      1
        elif isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      len(_SCREAMING_SNAKE_CASE   )
        else:
              raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_SCREAMING_SNAKE_CASE   )}"   )

        if height % 8 != 0 or width % 8 != 0:
              raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."   )

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) or callback_steps <= 0)
        ):
              raise ValueError(
                  F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                  F" {type(_SCREAMING_SNAKE_CASE   )}."   )

        # get prompt text embeddings
        UpperCamelCase       						=      self.tokenizer(
            _SCREAMING_SNAKE_CASE    ,	padding="""max_length"""    ,	max_length=self.tokenizer.model_max_length    ,	return_tensors="""pt"""    ,	)
        UpperCamelCase       						=      text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              UpperCamelCase       						=      self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]   )
              logger.warning(
                  """The following part of your input was truncated because CLIP can only handle sequences up to"""
                  F" {self.tokenizer.model_max_length} tokens: {removed_text}"   )
              UpperCamelCase       						=      text_input_ids[:, : self.tokenizer.model_max_length]

        if text_embeddings is None:
              UpperCamelCase       						=      self.text_encoder(text_input_ids.to(self.device   )   )[0]

        # duplicate text embeddings for each generation per prompt, using mps friendly method
        UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      text_embeddings.shape
        UpperCamelCase       						=      text_embeddings.repeat(1    ,	_SCREAMING_SNAKE_CASE    ,	1   )
        UpperCamelCase       						=      text_embeddings.view(bs_embed * num_images_per_prompt    ,	_SCREAMING_SNAKE_CASE    ,	-1   )

        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        UpperCamelCase       						=      guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
              UpperCamelCase       						=      42
              if negative_prompt is None:
                    UpperCamelCase       						=      [""""""]
              elif type(_SCREAMING_SNAKE_CASE   ) is not type(_SCREAMING_SNAKE_CASE   ):
                    raise TypeError(
                        F"`negative_prompt` should be the same type to `prompt`, but got {type(_SCREAMING_SNAKE_CASE   )} !="
                        F" {type(_SCREAMING_SNAKE_CASE   )}."   )
              elif isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
                    UpperCamelCase       						=      [negative_prompt]
              elif batch_size != len(_SCREAMING_SNAKE_CASE   ):
                    raise ValueError(
                        F"`negative_prompt`: {negative_prompt} has batch size {len(_SCREAMING_SNAKE_CASE   )}, but `prompt`:"
                        F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                        """ the batch size of `prompt`."""   )
              else:
                    UpperCamelCase       						=      negative_prompt

              UpperCamelCase       						=      text_input_ids.shape[-1]
              UpperCamelCase       						=      self.tokenizer(
                  _SCREAMING_SNAKE_CASE    ,	padding="""max_length"""    ,	max_length=_SCREAMING_SNAKE_CASE    ,	truncation=_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""    ,	)
              UpperCamelCase       						=      self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
              UpperCamelCase       						=      uncond_embeddings.shape[1]
              UpperCamelCase       						=      uncond_embeddings.repeat(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	1   )
              UpperCamelCase       						=      uncond_embeddings.view(batch_size * num_images_per_prompt    ,	_SCREAMING_SNAKE_CASE    ,	-1   )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              UpperCamelCase       						=      torch.cat([uncond_embeddings, text_embeddings]   )

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        UpperCamelCase       						=      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        UpperCamelCase       						=      (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
        UpperCamelCase       						=      text_embeddings.dtype
        if latents is None:
              if self.device.type == "mps":
                    # randn does not exist on mps
                    UpperCamelCase       						=      torch.randn(
                        _SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	device="""cpu"""    ,	dtype=_SCREAMING_SNAKE_CASE   ).to(self.device   )
                    UpperCamelCase       						=      torch.randn(_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	device="""cpu"""    ,	dtype=_SCREAMING_SNAKE_CASE   ).to(
                        self.device   )
              else:
                    UpperCamelCase       						=      torch.randn(
                        _SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	device=self.device    ,	dtype=_SCREAMING_SNAKE_CASE   )
                    UpperCamelCase       						=      torch.randn(_SCREAMING_SNAKE_CASE    ,	generator=_SCREAMING_SNAKE_CASE    ,	device=self.device    ,	dtype=_SCREAMING_SNAKE_CASE   )
        else:
              if latents_reference.shape != latents_shape:
                    raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"   )
              UpperCamelCase       						=      latents_reference.to(self.device   )
              UpperCamelCase       						=      latents.to(self.device   )

        # This is the key part of the pipeline where we
        # try to ensure that the generated images w/ the same seed
        # but different sizes actually result in similar images
        UpperCamelCase       						=      (latents_shape[3] - latents_shape_reference[3]) // 2
        UpperCamelCase       						=      (latents_shape[2] - latents_shape_reference[2]) // 2
        UpperCamelCase       						=      latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
        UpperCamelCase       						=      latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
        UpperCamelCase       						=      0 if dx < 0 else dx
        UpperCamelCase       						=      0 if dy < 0 else dy
        UpperCamelCase       						=      max(-dx    ,	0   )
        UpperCamelCase       						=      max(-dy    ,	0   )
        # import pdb
        # pdb.set_trace()
        UpperCamelCase       						=      latents_reference[:, :, dy : dy + h, dx : dx + w]

        # set timesteps
        self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE   )

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimized to move all timesteps to correct device beforehand
        UpperCamelCase       						=      self.scheduler.timesteps.to(self.device   )

        # scale the initial noise by the standard deviation required by the scheduler
        UpperCamelCase       						=      latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        UpperCamelCase       						=      """eta""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
        UpperCamelCase       						=      {}
        if accepts_eta:
              UpperCamelCase       						=      eta

        for i, t in enumerate(self.progress_bar(_SCREAMING_SNAKE_CASE   )   ):
              # expand the latents if we are doing classifier free guidance
              UpperCamelCase       						=      torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
              UpperCamelCase       						=      self.scheduler.scale_model_input(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # predict the noise residual
              UpperCamelCase       						=      self.unet(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	encoder_hidden_states=_SCREAMING_SNAKE_CASE   ).sample

              # perform guidance
              if do_classifier_free_guidance:
                    UpperCamelCase						,UpperCamelCase       						=      noise_pred.chunk(2   )
                    UpperCamelCase       						=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              UpperCamelCase       						=      self.scheduler.step(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE   ).prev_sample

              # call the callback, if provided
              if callback is not None and i % callback_steps == 0:
                    callback(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      1 / 0.1_8_2_1_5 * latents
        UpperCamelCase       						=      self.vae.decode(_SCREAMING_SNAKE_CASE   ).sample

        UpperCamelCase       						=      (image / 2 + 0.5).clamp(0    ,	1   )

        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
        UpperCamelCase       						=      image.cpu().permute(0    ,	2    ,	3    ,	1   ).float().numpy()

        if self.safety_checker is not None:
              UpperCamelCase       						=      self.feature_extractor(self.numpy_to_pil(_SCREAMING_SNAKE_CASE   )    ,	return_tensors="""pt"""   ).to(
                  self.device   )
              UpperCamelCase						,UpperCamelCase       						=      self.safety_checker(
                  images=_SCREAMING_SNAKE_CASE    ,	clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype   )   )
        else:
              UpperCamelCase       						=      None

        if output_type == "pil":
              UpperCamelCase       						=      self.numpy_to_pil(_SCREAMING_SNAKE_CASE   )

        if not return_dict:
              return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=_SCREAMING_SNAKE_CASE    ,	nsfw_content_detected=_SCREAMING_SNAKE_CASE   )








'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to 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
#
########################################################################


SCREAMING_SNAKE_CASE__											=     1_6
SCREAMING_SNAKE_CASE__											=     3_2







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase = 16					)->				Dict:
      UpperCamelCase       						=      AutoTokenizer.from_pretrained("""bert-base-cased"""					)
      UpperCamelCase       						=      load_dataset("""glue"""	,		"""mrpc"""					)

      def tokenize_function(__UpperCamelCase					):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase       						=      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():
            UpperCamelCase       						=      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
      UpperCamelCase       						=      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.
            UpperCamelCase       						=      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":
                  UpperCamelCase       						=      16
            elif accelerator.mixed_precision != "no":
                  UpperCamelCase       						=      8
            else:
                  UpperCamelCase       						=      None

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

      # Instantiate dataloaders.
      UpperCamelCase       						=      DataLoader(
          tokenized_datasets["""train"""]	,		shuffle=__UpperCamelCase	,		collate_fn=__UpperCamelCase	,		batch_size=__UpperCamelCase					)
      UpperCamelCase       						=      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

     SCREAMING_SNAKE_CASE__											=     mocked_dataloaders  # noqa: F811







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				List[Any]:
      # For testing only
      if os.environ.get("""TESTING_MOCKED_DATALOADERS"""	,		__UpperCamelCase					) == "1":
            UpperCamelCase       						=      2
      # New Code #
      UpperCamelCase       						=      int(args.gradient_accumulation_steps					)
      UpperCamelCase       						=      int(args.local_sgd_steps					)
      # Initialize accelerator
      UpperCamelCase       						=      Accelerator(
          cpu=args.cpu	,		mixed_precision=args.mixed_precision	,		gradient_accumulation_steps=__UpperCamelCase					)
      if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
            raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"""					)
      # 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"""]					)

      UpperCamelCase       						=      evaluate.load("""glue"""	,		"""mrpc"""					)

      set_seed(__UpperCamelCase					)
      UpperCamelCase						,UpperCamelCase       						=      get_dataloaders(__UpperCamelCase	,		__UpperCamelCase					)
      # 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=__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).
      UpperCamelCase       						=      model.to(accelerator.device					)

      # Instantiate optimizer
      UpperCamelCase       						=      AdamW(params=model.parameters()	,		lr=__UpperCamelCase					)

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

      # Now we train the model
      for epoch in range(__UpperCamelCase					):
            model.train()
            with LocalSGD(
                accelerator=__UpperCamelCase	,		model=__UpperCamelCase	,		local_sgd_steps=__UpperCamelCase	,		enabled=local_sgd_steps is not None					) as local_sgd:
                  for step, batch in enumerate(__UpperCamelCase					):
                        # 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(__UpperCamelCase					):
                              UpperCamelCase       						=      model(**__UpperCamelCase					)
                              UpperCamelCase       						=      output.loss
                              accelerator.backward(__UpperCamelCase					)
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                              # LocalSGD-specific line
                              local_sgd.step()

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

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







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      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."""	,		)
      # New Code #
      parser.add_argument(
          """--gradient_accumulation_steps"""	,		type=__UpperCamelCase	,		default=1	,		help="""The number of minibatches to be ran before gradients are accumulated."""	,		)
      parser.add_argument(
          """--local_sgd_steps"""	,		type=__UpperCamelCase	,		default=8	,		help="""Number of local SGD steps or None to disable local SGD"""					)
      parser.add_argument("""--cpu"""	,		action="""store_true"""	,		help="""If passed, will train on the CPU."""					)
      UpperCamelCase       						=      parser.parse_args()
      UpperCamelCase       						=      {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
      training_function(__UpperCamelCase	,		__UpperCamelCase					)


if __name__ == "__main__":
     main()
