code: 
  """simple docstring"""
  
  
  
  
  
  
  
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from ...models import UNetaDConditionModel, VQModel
  from ...pipelines import DiffusionPipeline
  from ...pipelines.pipeline_utils import ImagePipelineOutput
  from ...schedulers import DDPMScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  
  
  SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  SCREAMING_SNAKE_CASE_     				=   "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"
  def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		Union[str, Any]    ,  a_			:		Any=8				)     ->       str:
     _UpperCAmelCase   	=					height // scale_factor**2
     if height % scale_factor**2 != 0:
        new_height += 1
     _UpperCAmelCase   	=					width // scale_factor**2
     if width % scale_factor**2 != 0:
        new_width += 1
     return new_height * scale_factor, new_width * scale_factor
  def 			__UpperCAmelCase		(		a_			:		List[str]    ,  a_			:		Optional[int]=5_12    ,  a_			:		Tuple=5_12				)     ->       List[str]:
     _UpperCAmelCase   	=					pil_image.resize((w, h)    ,  resample=Image.BICUBIC    ,  reducing_gap=1				)
     _UpperCAmelCase   	=					np.array(pil_image.convert("RGB"				)				)
     _UpperCAmelCase   	=					arr.astype(np.floataa				) / 1_27.5 - 1
     _UpperCAmelCase   	=					np.transpose(_UpperCamelCase    ,  [2, 0, 1]				)
     _UpperCAmelCase   	=					torch.from_numpy(_UpperCamelCase				).unsqueeze(0				)
     return image
  
  
  
  
  
  
  class     __a			(					SCREAMING_SNAKE_CASE__			):
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            super().__init__()
  
            self.register_modules(
                unet=a_							,scheduler=a_							,movq=a_							,)
            _UpperCAmelCase   	=					2 ** (len(self.movq.config.block_out_channels		) - 1)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					min(int(num_inference_steps * strength		)							,a_		)
  
            _UpperCAmelCase   	=					max(num_inference_steps - init_timestep							,0		)
            _UpperCAmelCase   	=					self.scheduler.timesteps[t_start:]
  
            return timesteps, num_inference_steps - t_start
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=None		):
  
  
  
            '''simple docstring'''
  
            if not isinstance(a_							,(torch.Tensor, PIL.Image.Image, list)		):
               raise ValueError(
                   f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a_		)}'''		)
  
            _UpperCAmelCase   	=					image.to(device=a_							,dtype=a_		)
  
            _UpperCAmelCase   	=					batch_size * num_images_per_prompt
  
            if image.shape[1] == 4:
               _UpperCAmelCase   	=					image
  
            else:
               if isinstance(a_							,a_		) and len(a_		) != batch_size:
                  raise ValueError(
                      f'''You have passed a list of generators of length {len(a_		)}, but requested an effective batch'''
                      f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)
  
               elif isinstance(a_							,a_		):
                  _UpperCAmelCase   	=					[
                      self.movq.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(a_		)
                  ]
                  _UpperCAmelCase   	=					torch.cat(a_							,dim=0		)
               else:
                  _UpperCAmelCase   	=					self.movq.encode(a_		).latent_dist.sample(a_		)
  
               _UpperCAmelCase   	=					self.movq.config.scaling_factor * init_latents
  
            _UpperCAmelCase   	=					torch.cat([init_latents]							,dim=0		)
  
            _UpperCAmelCase   	=					init_latents.shape
            _UpperCAmelCase   	=					randn_tensor(a_							,generator=a_							,device=a_							,dtype=a_		)
  
            # get latents
            _UpperCAmelCase   	=					self.scheduler.add_noise(a_							,a_							,a_		)
  
            _UpperCAmelCase   	=					init_latents
  
            return latents
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_=0		):
  
  
  
            '''simple docstring'''
  
            if is_accelerate_available():
               from accelerate import cpu_offload
            else:
               raise ImportError("Please install accelerate via `pip install accelerate`"		)
  
            _UpperCAmelCase   	=					torch.device(f'''cuda:{gpu_id}'''		)
  
            _UpperCAmelCase   	=					[
                self.unet,
                self.movq,
            ]
            for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                  cpu_offload(a_							,a_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_=0		):
  
  
  
            '''simple docstring'''
  
            if is_accelerate_available() and is_accelerate_version(">="							,"0.17.0.dev0"		):
               from accelerate import cpu_offload_with_hook
            else:
               raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."		)
  
            _UpperCAmelCase   	=					torch.device(f'''cuda:{gpu_id}'''		)
  
            if self.device.type != "cpu":
               self.to("cpu"							,silence_dtype_warnings=a_		)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
            _UpperCAmelCase   	=					None
            for cpu_offloaded_model in [self.unet, self.movq]:
               _UpperCAmelCase	,							_UpperCAmelCase   	=					cpu_offload_with_hook(a_							,a_							,prev_module_hook=a_		)
  
            # We'll offload the last model manually.
            _UpperCAmelCase   	=					hook
  
  
  
  
  
  
  
         @property
         # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            if 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()
         @replace_example_docstring(a_		)
         def __call__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_ = 512							,lowerCamelCase_ = 512							,lowerCamelCase_ = 100							,lowerCamelCase_ = 4.0							,lowerCamelCase_ = 0.3							,lowerCamelCase_ = 1							,lowerCamelCase_ = None							,lowerCamelCase_ = "pil"							,lowerCamelCase_ = True							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self._execution_device
  
            _UpperCAmelCase   	=					guidance_scale > 1.0
  
            if isinstance(a_							,a_		):
               _UpperCAmelCase   	=					torch.cat(a_							,dim=0		)
            if isinstance(a_							,a_		):
               _UpperCAmelCase   	=					torch.cat(a_							,dim=0		)
            if isinstance(a_							,a_		):
               _UpperCAmelCase   	=					torch.cat(a_							,dim=0		)
  
            _UpperCAmelCase   	=					image_embeds.shape[0]
  
            if do_classifier_free_guidance:
               _UpperCAmelCase   	=					image_embeds.repeat_interleave(a_							,dim=0		)
               _UpperCAmelCase   	=					negative_image_embeds.repeat_interleave(a_							,dim=0		)
               _UpperCAmelCase   	=					hint.repeat_interleave(a_							,dim=0		)
  
            _UpperCAmelCase   	=					torch.cat([negative_image_embeds, image_embeds]							,dim=0		).to(dtype=self.unet.dtype							,device=a_		)
            _UpperCAmelCase   	=					torch.cat([hint, hint]							,dim=0		).to(dtype=self.unet.dtype							,device=a_		)
  
            if not isinstance(a_							,a_		):
               _UpperCAmelCase   	=					[image]
            if not all(isinstance(a_							,(PIL.Image.Image, torch.Tensor)		) for i in image		):
               raise ValueError(
                   f'''Input is in incorrect format: {[type(a_		) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''		)
  
            _UpperCAmelCase   	=					torch.cat([prepare_image(a_							,a_							,a_		) for i in image]							,dim=0		)
            _UpperCAmelCase   	=					image.to(dtype=image_embeds.dtype							,device=a_		)
  
            _UpperCAmelCase   	=					self.movq.encode(a_		)["latents"]
            _UpperCAmelCase   	=					latents.repeat_interleave(a_							,dim=0		)
            self.scheduler.set_timesteps(a_							,device=a_		)
            _UpperCAmelCase	,							_UpperCAmelCase   	=					self.get_timesteps(a_							,a_							,a_		)
            _UpperCAmelCase   	=					timesteps[:1].repeat(batch_size * num_images_per_prompt		)
            _UpperCAmelCase	,							_UpperCAmelCase   	=					downscale_height_and_width(a_							,a_							,self.movq_scale_factor		)
            _UpperCAmelCase   	=					self.prepare_latents(
                a_							,a_							,a_							,a_							,image_embeds.dtype							,a_							,a_		)
            for i, t in enumerate(self.progress_bar(a_		)		):
               # expand the latents if we are doing classifier free guidance
               _UpperCAmelCase   	=					torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
  
               _UpperCAmelCase   	=					{"image_embeds": image_embeds, "hint": hint}
               _UpperCAmelCase   	=					self.unet(
                   sample=a_							,timestep=a_							,encoder_hidden_states=a_							,added_cond_kwargs=a_							,return_dict=a_							,)[0]
  
               if do_classifier_free_guidance:
                  _UpperCAmelCase	,							_UpperCAmelCase   	=					noise_pred.split(latents.shape[1]							,dim=1		)
                  _UpperCAmelCase	,							_UpperCAmelCase   	=					noise_pred.chunk(2		)
                  _UpperCAmelCase	,							_UpperCAmelCase   	=					variance_pred.chunk(2		)
                  _UpperCAmelCase   	=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  _UpperCAmelCase   	=					torch.cat([noise_pred, variance_pred_text]							,dim=1		)
  
               if not (
                   hasattr(self.scheduler.config							,"variance_type"		)
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                  _UpperCAmelCase	,							_UpperCAmelCase   	=					noise_pred.split(latents.shape[1]							,dim=1		)
  
               # compute the previous noisy sample x_t -> x_t-1
  
               _UpperCAmelCase   	=					self.scheduler.step(
                   a_							,a_							,a_							,generator=a_							,)[0]
  
            # post-processing
            _UpperCAmelCase   	=					self.movq.decode(a_							,force_not_quantize=a_		)["sample"]
  
            if output_type not in ["pt", "np", "pil"]:
               raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''		)
  
            if output_type in ["np", "pil"]:
               _UpperCAmelCase   	=					image * 0.5 + 0.5
               _UpperCAmelCase   	=					image.clamp(0							,1		)
               _UpperCAmelCase   	=					image.cpu().permute(0							,2							,3							,1		).float().numpy()
  
            if output_type == "pil":
               _UpperCAmelCase   	=					self.numpy_to_pil(a_		)
  
            if not return_dict:
               return (image,)
  
            return ImagePipelineOutput(images=a_		)
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  
  
  import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
  
  
  
  
  
  
  class     __a			(					lowerCAmelCase_			):
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=False							,lowerCamelCase_=1024							,lowerCamelCase_=1024							,lowerCamelCase_=3.6							,lowerCamelCase_=False							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					tokenizer
            _UpperCAmelCase   	=					tokenizer.bos_token_id
            _UpperCAmelCase   	=					dataset
            _UpperCAmelCase   	=					seq_length
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					infinite
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					tokenized
  
            if self.tokenized:
               _UpperCAmelCase   	=					seq_length * num_of_sequences
               _UpperCAmelCase   	=					"input_ids"
            else:
               _UpperCAmelCase   	=					seq_length * chars_per_token * num_of_sequences
               _UpperCAmelCase   	=					"content"
  
  
  
  
  
  
  
         def __iter__(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					iter(self.dataset		)
            _UpperCAmelCase   	=					True
            while more_examples:
               _UpperCAmelCase	,							_UpperCAmelCase   	=					[], 0
               while True:
                  if buffer_len >= self.max_buffer_size:
                     break
                  try:
                     buffer.append(next(lowerCamelCase_		)[self.content_field]		)
                     buffer_len += len(buffer[-1]		)
                  except StopIteration:
                     if self.infinite:
                        _UpperCAmelCase   	=					iter(self.dataset		)
                        self.epoch += 1
                        logger.info(f'''Dataset epoch: {self.epoch}'''		)
                     else:
                        _UpperCAmelCase   	=					False
                        break
               if self.tokenized:
                  _UpperCAmelCase   	=					buffer
               else:
                  _UpperCAmelCase   	=					self.tokenizer(lowerCamelCase_							,truncation=lowerCamelCase_		)["input_ids"]
               _UpperCAmelCase   	=					[]
               for tokenized_input in tokenized_inputs:
                  all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
               for i in range(0							,len(lowerCamelCase_		)							,self.seq_length		):
                  _UpperCAmelCase   	=					all_token_ids[i : i + self.seq_length]
                  if len(lowerCamelCase_		) == self.seq_length:
                     self.current_size += 1
                     yield torch.tensor(lowerCamelCase_		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_=1000		):
  
  
  
            '''simple docstring'''
  
            return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)
  
  
  
  def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
     _UpperCAmelCase   	=					args.model_ckpt.split("/"				)[-1]
     _UpperCAmelCase   	=					logging.getLogger(__name__				)
     _UpperCAmelCase   	=					Path(args.save_dir				) / "log/"
     log_dir.mkdir(exist_ok=a_				)
     _UpperCAmelCase   	=					F'''debug_{accelerator.process_index}.log'''
     logging.basicConfig(
         format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,  datefmt="%m/%d/%Y %H:%M:%S"    ,  level=logging.INFO    ,  handlers=[logging.FileHandler(log_dir / filename				), logging.StreamHandler()]    ,  )
     if accelerator.is_main_process:  # we only want to setup logging once
        accelerator.init_trackers(a_    ,  vars(a_				)				)
        _UpperCAmelCase   	=					accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO				)
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
     else:
        _UpperCAmelCase   	=					""
        logger.setLevel(logging.ERROR				)
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
     return logger, run_name
  def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
     _UpperCAmelCase   	=					{"streaming": True}
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_train    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					train_data.shuffle(buffer_size=args.shuffle_buffer    ,  seed=args.seed				)
     _UpperCAmelCase   	=					load_dataset(args.dataset_name_valid    ,  split="train"    ,  **a_				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					ConstantLengthDataset(
         a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
     _UpperCAmelCase   	=					train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.train_batch_size    ,  shuffle=a_				)
     _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.valid_batch_size				)
     return train_dataloader, eval_dataloader
  def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
     _UpperCAmelCase	,							_UpperCAmelCase   	=					[], []
     for n, p in model.named_parameters():
        if any(nd in n for nd in no_decay				):
           params_without_wd.append(a_				)
        else:
           params_with_wd.append(a_				)
     return [
         {"params": params_with_wd, "weight_decay": args.weight_decay},
         {"params": params_without_wd, "weight_decay": 0.0},
     ]
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[str]				)     ->       Optional[Any]:
     logger.info(F'''Step {step}: {metrics}'''				)
     if accelerator.is_main_process:
        accelerator.log(a_    ,  a_				)
  def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       int:
     # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
     _UpperCAmelCase   	=					accelerator.unwrap_model(a_				).config
     _UpperCAmelCase   	=					4 if args.gradient_checkpointing else 3
     _UpperCAmelCase   	=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
     _UpperCAmelCase   	=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
     _UpperCAmelCase   	=					factor * (
         1.0
         + (args.seq_length / (6.0 * config_model.n_embd))
         + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
     )
     _UpperCAmelCase   	=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
     return tflops
  def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
     model.eval()
     _UpperCAmelCase   	=					[]
     for step, batch in enumerate(a_				):
        with torch.no_grad():
           _UpperCAmelCase   	=					model(a_    ,  labels=a_				)
        _UpperCAmelCase   	=					outputs.loss.repeat(args.valid_batch_size				)
        losses.append(accelerator.gather(a_				)				)
        if args.max_eval_steps > 0 and step >= args.max_eval_steps:
           break
     _UpperCAmelCase   	=					torch.cat(a_				)
     _UpperCAmelCase   	=					losses[: eval_dataloader.dataset.current_size].mean()
     try:
        _UpperCAmelCase   	=					torch.exp(a_				)
     except OverflowError:
        _UpperCAmelCase   	=					float("inf"				)
     return loss.item(), perplexity.item()
  
  
  # Settings
  SCREAMING_SNAKE_CASE_     				=   HfArgumentParser(TrainingArguments)
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()
  
  # Accelerator
  SCREAMING_SNAKE_CASE_     				=   ProjectConfiguration(project_dir=args.save_dir, logging_dir='log')
  SCREAMING_SNAKE_CASE_     				=   Accelerator(log_with=['wandb', 'tensorboard'], project_config=config)
  SCREAMING_SNAKE_CASE_     				=   {str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  SCREAMING_SNAKE_CASE_     				=   Namespace(**vars(args), **acc_state)
  SCREAMING_SNAKE_CASE_     				=   accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
    SCREAMING_SNAKE_CASE_     				=   Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   setup_logging(args)
  logger.info(accelerator.state)
  
  # Checkout new branch on repo
  if accelerator.is_main_process:
    hf_repo.git_checkout(run_name, create_branch_ok=True)
  
  # Load model and tokenizer
  SCREAMING_SNAKE_CASE_     				=   AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
    model.gradient_checkpointing_enable()
  SCREAMING_SNAKE_CASE_     				=   AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  SCREAMING_SNAKE_CASE_     				=   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  SCREAMING_SNAKE_CASE_     				=   get_scheduler(
      name=args.lr_scheduler_type,
      optimizer=optimizer,
      num_warmup_steps=args.num_warmup_steps,
      num_training_steps=args.max_train_steps,
  )
  accelerator.register_for_checkpointing(lr_scheduler)
  def 			__UpperCAmelCase		(		)     ->       int:
     return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   accelerator.prepare(
      model, optimizer, train_dataloader, eval_dataloader
  )
  
  # load in the weights and states from a previous save
  if args.resume_from_checkpoint:
    if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
      accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
      accelerator.load_state(args.resume_from_checkpoint)
      SCREAMING_SNAKE_CASE_     				=   os.path.basename(args.resume_from_checkpoint)
    else:
      # Get the most recent checkpoint
      SCREAMING_SNAKE_CASE_     				=   [f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
      dirs.sort(key=os.path.getctime)
      SCREAMING_SNAKE_CASE_     				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract the step of the checkpoint to continue from there
    SCREAMING_SNAKE_CASE_     				=   os.path.splitext(path)[0]
    SCREAMING_SNAKE_CASE_     				=   int(training_difference.replace('step_', ''))
  
  # Train model
  model.train()
  SCREAMING_SNAKE_CASE_     				=   0
  SCREAMING_SNAKE_CASE_     				=   time.time()
  SCREAMING_SNAKE_CASE_     				=   0
  for step, batch in enumerate(train_dataloader, start=1):
    if args.resume_from_checkpoint and step < resume_step:
      continue  # we need to skip steps until we reach the resumed step
    SCREAMING_SNAKE_CASE_     				=   model(batch, labels=batch, use_cache=False).loss
    SCREAMING_SNAKE_CASE_     				=   accelerator.gather(loss.repeat(args.train_batch_size)).mean()
    loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
    log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
    SCREAMING_SNAKE_CASE_     				=   loss / args.gradient_accumulation_steps
    if step % args.gradient_accumulation_steps != 0:
      # Prevent backward from doing gradient all_reduce in every step
      if accelerator.distributed_type == DistributedType.MULTI_GPU:
        with model.no_sync():
          accelerator.backward(loss)
      else:
        accelerator.backward(loss)
    else:
      SCREAMING_SNAKE_CASE_     				=   get_lr()
      accelerator.backward(loss)
      accelerator.clip_grad_norm_(model.parameters(), 1.0)
      optimizer.step()
      lr_scheduler.step()
      optimizer.zero_grad()
      SCREAMING_SNAKE_CASE_     				=   time.time() - t_start
      SCREAMING_SNAKE_CASE_     				=   compute_tflops(elapsed_time, accelerator, args)
      log_metrics(
          step,
          {
              'steps': completed_steps,
              'loss/train': loss_tracking,
              'lr': lr,
              'tflops': tflops,
              'time_per_iteration': elapsed_time,
          },
      )
      SCREAMING_SNAKE_CASE_     				=   time.time()
      SCREAMING_SNAKE_CASE_     				=   0
      completed_steps += 1
    if step % args.save_checkpoint_steps == 0:
      logger.info('Evaluating and saving model checkpoint')
      SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
      log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
      accelerator.wait_for_everyone()
      SCREAMING_SNAKE_CASE_     				=   os.path.join(args.save_dir, f'''step_{step}''')
      accelerator.save_state(save_dir)
      if accelerator.is_main_process:
        hf_repo.push_to_hub(commit_message=f'''step {step}''')
      model.train()
    if completed_steps >= args.max_train_steps:
      break
  
  # Evaluate and save the last checkpoint
  logger.info('Evaluating and saving model after training')
  SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
  log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
  accelerator.wait_for_everyone()
  SCREAMING_SNAKE_CASE_     				=   accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  SCREAMING_SNAKE_CASE_     				=   os.path.join(args.save_dir, f'''step_{step}''')
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
    hf_repo.push_to_hub(commit_message='final model')
  
  