





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def    UpperCAmelCase				(  _lowerCamelCase   :	List[str]      , _lowerCamelCase   :	str=0.9_9_9      , _lowerCamelCase   :	Union[str, Any]="cosine"      , ):





						'''simple docstring'''


						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_lowerCamelCase   :	str  ):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2  ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_lowerCamelCase   :	int  ):
																		return math.exp(t * -1_2.0  )

						else:
												raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""  )

						SCREAMING_SNAKE_CASE__  :					int =       []
						for i in range(_lowerCamelCase  ):
												SCREAMING_SNAKE_CASE__  :					Dict =       i / num_diffusion_timesteps
												SCREAMING_SNAKE_CASE__  :					int =       (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_lowerCamelCase  ) / alpha_bar_fn(_lowerCamelCase  )      , _lowerCamelCase  )  )
						return torch.tensor(_lowerCamelCase      , dtype=torch.floataa  )



class  _a  (					lowercase__       ,				lowercase__				):






							"""simple docstring"""

							snake_case_							=    [e.name for e in KarrasDiffusionSchedulers]
							snake_case_							=    2

							@register_to_config
							def __init__(   self					:  List[str]				,						a					:  int = 10_00				,						a					:  float = 0.0_0085				,						a					:  float = 0.012				,						a					:  str = "linear"				,						a					:  Optional[Union[np.ndarray, List[float]]] = None				,						a					:  str = "epsilon"				,						a					:  str = "linspace"				,						a					:  int = 0				,						)						->Optional[Any]:
													if trained_betas is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       torch.tensor(a				,						dtype=torch.floataa     )
													elif beta_schedule == "linear":
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       torch.linspace(a				,						a				,						a				,						dtype=torch.floataa     )
													elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			SCREAMING_SNAKE_CASE__  :					Dict =       (
																			    torch.linspace(beta_start**0.5				,						beta_end**0.5				,						a				,						dtype=torch.floataa     ) ** 2
																			)
													elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			SCREAMING_SNAKE_CASE__  :					List[str] =       betas_for_alpha_bar(a     )
													else:
																			raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       1.0 - self.betas
													SCREAMING_SNAKE_CASE__  :					Tuple =       torch.cumprod(self.alphas				,						dim=0     )

													#  set all values
													self.set_timesteps(a				,						a				,						a     )

							def   A_	(   self					:  str				,						a					:  Optional[Any]				,						a					:  Optional[int]=None     )						->List[Any]:
													if schedule_timesteps is None:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       self.timesteps

													SCREAMING_SNAKE_CASE__  :					str =       (schedule_timesteps == timestep).nonzero()

													# The sigma index that is taken for the **very** first `step`
													# is always the second index (or the last index if there is only 1)
													# This way we can ensure we don't accidentally skip a sigma in
													# case we start in the middle of the denoising schedule (e.g. for image-to-image)
													if len(self._index_counter     ) == 0:
																			SCREAMING_SNAKE_CASE__  :					Any =       1 if len(a     ) > 1 else 0
													else:
																			SCREAMING_SNAKE_CASE__  :					str =       timestep.cpu().item() if torch.is_tensor(a     ) else timestep
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self._index_counter[timestep_int]

													return indices[pos].item()

							@property
							def   A_	(   self					:  Union[str, Any]     )						->List[str]:
													# standard deviation of the initial noise distribution
													if self.config.timestep_spacing in ["linspace", "trailing"]:
																			return self.sigmas.max()

													return (self.sigmas.max() ** 2 + 1) ** 0.5

							def   A_	(   self					:  Any				,						a					:  torch.FloatTensor				,						a					:  Union[float, torch.FloatTensor]				,						)						->torch.FloatTensor:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.index_for_timestep(a     )

													if self.state_in_first_order:
																			SCREAMING_SNAKE_CASE__  :					int =       self.sigmas[step_index]
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.sigmas_interpol[step_index - 1]

													SCREAMING_SNAKE_CASE__  :					Any =       sample / ((sigma**2 + 1) ** 0.5)
													return sample

							def   A_	(   self					:  Tuple				,						a					:  int				,						a					:  Union[str, torch.device] = None				,						a					:  Optional[int] = None				,						)						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					str =       num_inference_steps

													SCREAMING_SNAKE_CASE__  :					Any =       num_train_timesteps or self.config.num_train_timesteps

													# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
													if self.config.timestep_spacing == "linspace":
																			SCREAMING_SNAKE_CASE__  :					Dict =       np.linspace(0				,						num_train_timesteps - 1				,						a				,						dtype=a     )[::-1].copy()
													elif self.config.timestep_spacing == "leading":
																			SCREAMING_SNAKE_CASE__  :					List[str] =       num_train_timesteps // self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       (np.arange(0				,						a     ) * step_ratio).round()[::-1].copy().astype(a     )
																			timesteps += self.config.steps_offset
													elif self.config.timestep_spacing == "trailing":
																			SCREAMING_SNAKE_CASE__  :					Tuple =       num_train_timesteps / self.num_inference_steps
																			# creates integer timesteps by multiplying by ratio
																			# casting to int to avoid issues when num_inference_step is power of 3
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       (np.arange(a				,						0				,						-step_ratio     )).round().copy().astype(a     )
																			timesteps -= 1
													else:
																			raise ValueError(
																			    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""     )

													SCREAMING_SNAKE_CASE__  :					Dict =       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5     )
													SCREAMING_SNAKE_CASE__  :					Dict =       torch.from_numpy(np.log(a     )     ).to(a     )

													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       np.interp(a				,						np.arange(0				,						len(a     )     )				,						a     )
													SCREAMING_SNAKE_CASE__  :					Dict =       np.concatenate([sigmas, [0.0]]     ).astype(np.floataa     )
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.from_numpy(a     ).to(device=a     )

													# compute up and down sigmas
													SCREAMING_SNAKE_CASE__  :					Tuple =       sigmas.roll(-1     )
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       0.0
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
													SCREAMING_SNAKE_CASE__  :					Dict =       (sigmas_next**2 - sigmas_up**2) ** 0.5
													SCREAMING_SNAKE_CASE__  :					List[str] =       0.0

													# compute interpolated sigmas
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       sigmas.log().lerp(sigmas_down.log()				,						0.5     ).exp()
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       0.0

													# set sigmas
													SCREAMING_SNAKE_CASE__  :					str =       torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       torch.cat(
													    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2     ), sigmas_interpol[-1:]]     )
													SCREAMING_SNAKE_CASE__  :					str =       torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2     ), sigmas_up[-1:]]     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2     ), sigmas_down[-1:]]     )

													if str(a     ).startswith("mps"     ):
																			# mps does not support float64
																			SCREAMING_SNAKE_CASE__  :					Dict =       torch.from_numpy(a     ).to(a				,						dtype=torch.floataa     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       torch.from_numpy(a     ).to(a     )

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.sigma_to_t(a     ).to(a				,						dtype=timesteps.dtype     )
													SCREAMING_SNAKE_CASE__  :					Dict =       torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])				,						dim=-1     ).flatten()

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       torch.cat([timesteps[:1], interleaved_timesteps]     )

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       None

													# for exp beta schedules, such as the one for `pipeline_shap_e.py`
													# we need an index counter
													SCREAMING_SNAKE_CASE__  :					int =       defaultdict(a     )

							def   A_	(   self					:  Optional[int]				,						a					:  Union[str, Any]     )						->List[Any]:
													# get log sigma
													SCREAMING_SNAKE_CASE__  :					List[Any] =       sigma.log()

													# get distribution
													SCREAMING_SNAKE_CASE__  :					Any =       log_sigma - self.log_sigmas[:, None]

													# get sigmas range
													SCREAMING_SNAKE_CASE__  :					int =       dists.ge(0     ).cumsum(dim=0     ).argmax(dim=0     ).clamp(max=self.log_sigmas.shape[0] - 2     )
													SCREAMING_SNAKE_CASE__  :					str =       low_idx + 1

													SCREAMING_SNAKE_CASE__  :					Dict =       self.log_sigmas[low_idx]
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self.log_sigmas[high_idx]

													# interpolate sigmas
													SCREAMING_SNAKE_CASE__  :					int =       (low - log_sigma) / (low - high)
													SCREAMING_SNAKE_CASE__  :					List[Any] =       w.clamp(0				,						1     )

													# transform interpolation to time range
													SCREAMING_SNAKE_CASE__  :					List[Any] =       (1 - w) * low_idx + w * high_idx
													SCREAMING_SNAKE_CASE__  :					Tuple =       t.view(sigma.shape     )
													return t

							@property
							def   A_	(   self					:  Union[str, Any]     )						->Tuple:
													return self.sample is None

							def   A_	(   self					:  Tuple				,						a					:  Union[torch.FloatTensor, np.ndarray]				,						a					:  Union[float, torch.FloatTensor]				,						a					:  Union[torch.FloatTensor, np.ndarray]				,						a					:  Optional[torch.Generator] = None				,						a					:  bool = True				,						)						->Union[SchedulerOutput, Tuple]:
													SCREAMING_SNAKE_CASE__  :					str =       self.index_for_timestep(a     )

													# advance index counter by 1
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       timestep.cpu().item() if torch.is_tensor(a     ) else timestep
													self._index_counter[timestep_int] += 1

													if self.state_in_first_order:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.sigmas[step_index]
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.sigmas_interpol[step_index]
																			SCREAMING_SNAKE_CASE__  :					Any =       self.sigmas_up[step_index]
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.sigmas_down[step_index - 1]
													else:
																			# 2nd order / KPDM2's method
																			SCREAMING_SNAKE_CASE__  :					Any =       self.sigmas[step_index - 1]
																			SCREAMING_SNAKE_CASE__  :					str =       self.sigmas_interpol[step_index - 1]
																			SCREAMING_SNAKE_CASE__  :					int =       self.sigmas_up[step_index - 1]
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.sigmas_down[step_index - 1]

													# currently only gamma=0 is supported. This usually works best anyways.
													# We can support gamma in the future but then need to scale the timestep before
													# passing it to the model which requires a change in API
													SCREAMING_SNAKE_CASE__  :					Dict =       0
													SCREAMING_SNAKE_CASE__  :					str =       sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

													SCREAMING_SNAKE_CASE__  :					Tuple =       model_output.device
													SCREAMING_SNAKE_CASE__  :					int =       randn_tensor(model_output.shape				,						dtype=model_output.dtype				,						device=a				,						generator=a     )

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			SCREAMING_SNAKE_CASE__  :					Tuple =       sigma_hat if self.state_in_first_order else sigma_interpol
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       sample - sigma_input * model_output
													elif self.config.prediction_type == "v_prediction":
																			SCREAMING_SNAKE_CASE__  :					Any =       sigma_hat if self.state_in_first_order else sigma_interpol
																			SCREAMING_SNAKE_CASE__  :					List[str] =       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																			    sample / (sigma_input**2 + 1)
																			)
													elif self.config.prediction_type == "sample":
																			raise NotImplementedError("prediction_type not implemented yet: sample"     )
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""     )

													if self.state_in_first_order:
																			# 2. Convert to an ODE derivative for 1st order
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       (sample - pred_original_sample) / sigma_hat
																			# 3. delta timestep
																			SCREAMING_SNAKE_CASE__  :					Any =       sigma_interpol - sigma_hat

																			# store for 2nd order step
																			SCREAMING_SNAKE_CASE__  :					str =       sample
																			SCREAMING_SNAKE_CASE__  :					List[str] =       dt
																			SCREAMING_SNAKE_CASE__  :					Tuple =       sample + derivative * dt
													else:
																			# DPM-Solver-2
																			# 2. Convert to an ODE derivative for 2nd order
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       (sample - pred_original_sample) / sigma_interpol
																			# 3. delta timestep
																			SCREAMING_SNAKE_CASE__  :					List[str] =       sigma_down - sigma_hat

																			SCREAMING_SNAKE_CASE__  :					str =       self.sample
																			SCREAMING_SNAKE_CASE__  :					Tuple =       None

																			SCREAMING_SNAKE_CASE__  :					Any =       sample + derivative * dt
																			SCREAMING_SNAKE_CASE__  :					int =       prev_sample + noise * sigma_up

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=a     )

							def   A_	(   self					:  Tuple				,						a					:  torch.FloatTensor				,						a					:  torch.FloatTensor				,						a					:  torch.FloatTensor				,						)						->torch.FloatTensor:
													# Make sure sigmas and timesteps have the same device and dtype as original_samples
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.sigmas.to(device=original_samples.device				,						dtype=original_samples.dtype     )
													if original_samples.device.type == "mps" and torch.is_floating_point(a     ):
																			# mps does not support float64
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.timesteps.to(original_samples.device				,						dtype=torch.floataa     )
																			SCREAMING_SNAKE_CASE__  :					Dict =       timesteps.to(original_samples.device				,						dtype=torch.floataa     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.timesteps.to(original_samples.device     )
																			SCREAMING_SNAKE_CASE__  :					Dict =       timesteps.to(original_samples.device     )

													SCREAMING_SNAKE_CASE__  :					Tuple =       [self.index_for_timestep(a				,						a     ) for t in timesteps]

													SCREAMING_SNAKE_CASE__  :					Tuple =       sigmas[step_indices].flatten()
													while len(sigma.shape     ) < len(original_samples.shape     ):
																			SCREAMING_SNAKE_CASE__  :					Any =       sigma.unsqueeze(-1     )

													SCREAMING_SNAKE_CASE__  :					int =       original_samples + noise * sigma
													return noisy_samples






							def __len__(   self					:  List[Any]     )						->str:
													return self.config.num_train_timesteps






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  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  int				,						a					:  List[Any]				,						a					:  Tuple				,						a					:  Dict=False				,						a					:  Union[str, Any]=10_24				,						a					:  List[str]=10_24				,						a					:  Optional[Any]=3.6				,						a					:  List[Any]=False				,						)						->List[str]:
													SCREAMING_SNAKE_CASE__  :					str =       tokenizer
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       tokenizer.bos_token_id
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       dataset
													SCREAMING_SNAKE_CASE__  :					List[Any] =       seq_length
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       0
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       infinite
													SCREAMING_SNAKE_CASE__  :					Tuple =       0
													SCREAMING_SNAKE_CASE__  :					Dict =       tokenized

													if self.tokenized:
																			SCREAMING_SNAKE_CASE__  :					Tuple =       seq_length * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Dict =       "input_ids"
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       seq_length * chars_per_token * num_of_sequences
																			SCREAMING_SNAKE_CASE__  :					Any =       "content"

							def __iter__(   self					:  Union[str, Any]     )						->List[str]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       iter(self.dataset     )
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       True
													while more_examples:
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       [], 0
																			while True:
																									if buffer_len >= self.max_buffer_size:
																															break
																									try:
																															buffer.append(next(a     )[self.content_field]     )
																															buffer_len += len(buffer[-1]     )
																									except StopIteration:
																															if self.infinite:
																																					SCREAMING_SNAKE_CASE__  :					Dict =       iter(self.dataset     )
																																					self.epoch += 1
																																					logger.info(f"""Dataset epoch: {self.epoch}"""     )
																															else:
																																					SCREAMING_SNAKE_CASE__  :					int =       False
																																					break
																			if self.tokenized:
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       buffer
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer(a				,						truncation=a     )["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Tuple =       []
																			for tokenized_input in tokenized_inputs:
																									all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
																			for i in range(0				,						len(a     )				,						self.seq_length     ):
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       all_token_ids[i : i + self.seq_length]
																									if len(a     ) == self.seq_length:
																															self.current_size += 1
																															yield torch.tensor(a     )






							def   A_	(   self					:  Optional[Any]				,						a					:  Optional[Any]=10_00     )						->Union[str, Any]:
													return ShufflerIterDataPipe(self				,						buffer_size=a     )







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       args.model_ckpt.split("/"  )[-1]
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       logging.getLogger(__name__  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       Path(args.save_dir  ) / "log/"
						log_dir.mkdir(exist_ok=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       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(_lowerCamelCase      , vars(_lowerCamelCase  )  )
												SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.trackers[0].run.name
												logger.setLevel(logging.INFO  )
												datasets.utils.logging.set_verbosity_info()
												transformers.utils.logging.set_verbosity_info()
						else:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       ""
												logger.setLevel(logging.ERROR  )
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()
						return logger, run_name







def    UpperCAmelCase				(  _lowerCamelCase   :	Dict  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[str] =       {"streaming": True}
						SCREAMING_SNAKE_CASE__  :					str =       load_dataset(args.dataset_name_train      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					str =       train_data.shuffle(buffer_size=args.shuffle_buffer      , seed=args.seed  )
						SCREAMING_SNAKE_CASE__  :					Any =       load_dataset(args.dataset_name_valid      , split="train"      , **_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Any =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					str =       ConstantLengthDataset(
						    _lowerCamelCase      , _lowerCamelCase      , infinite=_lowerCamelCase      , seq_length=args.seq_length      , tokenized=args.tokenized  )
						SCREAMING_SNAKE_CASE__  :					Any =       train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
						SCREAMING_SNAKE_CASE__  :					Dict =       DataLoader(_lowerCamelCase      , batch_size=args.train_batch_size      , shuffle=_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					List[Any] =       DataLoader(_lowerCamelCase      , batch_size=args.valid_batch_size  )
						return train_dataloader, eval_dataloader







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	List[Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					int =       [], []
						for n, p in model.named_parameters():
												if any(nd in n for nd in no_decay  ):
																		params_without_wd.append(_lowerCamelCase  )
												else:
																		params_with_wd.append(_lowerCamelCase  )
						return [
						    {"params": params_with_wd, "weight_decay": args.weight_decay},
						    {"params": params_without_wd, "weight_decay": 0.0},
						]







def    UpperCAmelCase				(  _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						logger.info(f"""Step {step}: {metrics}"""  )
						if accelerator.is_main_process:
												accelerator.log(_lowerCamelCase      , _lowerCamelCase  )







def    UpperCAmelCase				(  _lowerCamelCase   :	Tuple      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[Any]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Optional[int] =       accelerator.unwrap_model(_lowerCamelCase  ).config
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       4 if args.gradient_checkpointing else 3
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       factor * (
						    1.0
						    + (args.seq_length / (6.0 * config_model.n_embd))
						    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
						)
						SCREAMING_SNAKE_CASE__  :					Tuple =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
						return tflops







def    UpperCAmelCase				(  _lowerCamelCase   :	List[Any]  ):





						'''simple docstring'''


						model.eval()
						SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
						for step, batch in enumerate(_lowerCamelCase  ):
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE__  :					List[str] =       model(_lowerCamelCase      , labels=_lowerCamelCase  )
												SCREAMING_SNAKE_CASE__  :					Any =       outputs.loss.repeat(args.valid_batch_size  )
												losses.append(accelerator.gather(_lowerCamelCase  )  )
												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						SCREAMING_SNAKE_CASE__  :					str =       torch.cat(_lowerCamelCase  )
						SCREAMING_SNAKE_CASE__  :					Dict =       losses[: eval_dataloader.dataset.current_size].mean()
						try:
												SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.exp(_lowerCamelCase  )
						except OverflowError:
												SCREAMING_SNAKE_CASE__  :					Optional[Any] =       float("inf"  )
						return loss.item(), perplexity.item()


# Settings
__lowercase							:List[Any]							=	HfArgumentParser(TrainingArguments)
__lowercase							:Any							=	parser.parse_args()

# Accelerator
__lowercase							:List[Any]							=	ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
__lowercase							:Optional[int]							=	Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
__lowercase							:str							=	{str(k): str(v) for k, v in accelerator.state.__dict__.items()}

__lowercase							:List[Any]							=	Namespace(**vars(args), **acc_state)
__lowercase							:Optional[Any]							=	accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
				__lowercase							:Dict							=	Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
__lowercase							,  __lowercase							:Union[str, Any]							=	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
__lowercase							:Tuple							=	AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
__lowercase							:Union[str, Any]							=	AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
__lowercase							,  __lowercase							:Any							=	create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
__lowercase							:int							=	AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase							:str							=	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				(  ):





						'''simple docstring'''


						return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
__lowercase							,  __lowercase							,  __lowercase							,  __lowercase							:Dict							=	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)
								__lowercase							:List[str]							=	os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								__lowercase							:Union[str, Any]							=	[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)
								__lowercase							:Dict							=	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				__lowercase							:Any							=	os.path.splitext(path)[0]
				__lowercase							:str							=	int(training_difference.replace("step_", ""))

# Train model
model.train()
__lowercase							:int							=	0
__lowercase							:List[Any]							=	time.time()
__lowercase							:Any							=	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
				__lowercase							:List[str]							=	model(batch, labels=batch, use_cache=False).loss
				__lowercase							:Union[str, Any]							=	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()})
				__lowercase							:List[Any]							=	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:
								__lowercase							:List[Any]							=	get_lr()
								accelerator.backward(loss)
								accelerator.clip_grad_norm_(model.parameters(), 1.0)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
								__lowercase							:str							=	time.time() - t_start
								__lowercase							:List[str]							=	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,
								    },
								)
								__lowercase							:str							=	time.time()
								__lowercase							:Optional[int]							=	0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("Evaluating and saving model checkpoint")
								__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
								log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
								accelerator.wait_for_everyone()
								__lowercase							:int							=	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")
__lowercase							,  __lowercase							:List[str]							=	evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
__lowercase							:List[str]							=	accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
__lowercase							:List[Any]							=	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")