
"""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_		)



"""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')

