






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        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Dict ,							snake_case__:						Optional[Any] ,							snake_case__:						Union[str, Any] ,							snake_case__:						int=False ,							snake_case__:						Tuple=1_024 ,							snake_case__:						Union[str, Any]=1_024 ,							snake_case__:						List[str]=3.6 ,							snake_case__:						Tuple=False ,							)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						tokenizer
       _lowercase     			=						tokenizer.bos_token_id
       _lowercase     			=						dataset
       _lowercase     			=						seq_length
       _lowercase     			=						0
       _lowercase     			=						infinite
       _lowercase     			=						0
       _lowercase     			=						tokenized

       if self.tokenized:
           _lowercase     			=						seq_length * num_of_sequences
           _lowercase     			=						'''input_ids'''
       else:
           _lowercase     			=						seq_length * chars_per_token * num_of_sequences
           _lowercase     			=						'''content'''

   def __iter__(     self:						List[Any]						)	->		int:


       """simple docstring"""



       _lowercase     			=						iter(self.dataset						)
       _lowercase     			=						True
       while more_examples:
           _lowercase     			=						[], 0
           while True:
               if buffer_len >= self.max_buffer_size:
                   break
               try:
                   buffer.append(next(SCREAMING_SNAKE_CASE_						)[self.content_field]						)
                   buffer_len += len(buffer[-1]						)
               except StopIteration:
                   if self.infinite:
                       _lowercase     			=						iter(self.dataset						)
                       self.epoch += 1
                       logger.info(f'''Dataset epoch: {self.epoch}'''						)
                   else:
                       _lowercase     			=						False
                       break
           if self.tokenized:
               _lowercase     			=						buffer
           else:
               _lowercase     			=						self.tokenizer(SCREAMING_SNAKE_CASE_ ,							truncation=SCREAMING_SNAKE_CASE_						)['''input_ids''']
           _lowercase     			=						[]
           for tokenized_input in tokenized_inputs:
               all_token_ids.extend(tokenized_input + [self.concat_token_id]						)
           for i in range(0 ,							len(SCREAMING_SNAKE_CASE_						) ,							self.seq_length						):
               _lowercase     			=						all_token_ids[i : i + self.seq_length]
               if len(SCREAMING_SNAKE_CASE_						) == self.seq_length:
                   self.current_size += 1
                   yield torch.tensor(SCREAMING_SNAKE_CASE_						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						Any=1_000						)	->		Optional[int]:


       """simple docstring"""



       return ShufflerIterDataPipe(self ,							buffer_size=SCREAMING_SNAKE_CASE_						)






def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						args.model_ckpt.split('/'   )[-1]
    _lowercase     			=						logging.getLogger(__name__   )
    _lowercase     			=						Path(args.save_dir   ) / '''log/'''
    log_dir.mkdir(exist_ok=A_   )
    _lowercase     			=						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_   )   )
        _lowercase     			=						accelerator.trackers[0].run.name
        logger.setLevel(logging.INFO   )
        datasets.utils.logging.set_verbosity_info()
        transformers.utils.logging.set_verbosity_info()
    else:
        _lowercase     			=						''''''
        logger.setLevel(logging.ERROR   )
        datasets.utils.logging.set_verbosity_error()
        transformers.utils.logging.set_verbosity_error()
    return logger, run_name
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						{'''streaming''': True}
    _lowercase     			=						load_dataset(args.dataset_name_train,   split='train',   **A_   )
    _lowercase     			=						train_data.shuffle(buffer_size=args.shuffle_buffer,   seed=args.seed   )
    _lowercase     			=						load_dataset(args.dataset_name_valid,   split='train',   **A_   )
    _lowercase     			=						ConstantLengthDataset(
        A_,   A_,   infinite=A_,   seq_length=args.seq_length,   tokenized=args.tokenized   )
    _lowercase     			=						ConstantLengthDataset(
        A_,   A_,   infinite=A_,   seq_length=args.seq_length,   tokenized=args.tokenized   )
    _lowercase     			=						train_dataset.shuffle(buffer_size=args.shuffle_buffer   )
    _lowercase     			=						DataLoader(A_,   batch_size=args.train_batch_size,   shuffle=A_   )
    _lowercase     			=						DataLoader(A_,   batch_size=args.valid_batch_size   )
    return train_dataloader, eval_dataloader
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   ):


    '''simple docstring'''



    _lowercase     			=						[], []
    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   ( _lowercase,   _lowercase   ):


    '''simple docstring'''



    logger.info(f'''Step {step}: {metrics}'''   )
    if accelerator.is_main_process:
        accelerator.log(A_,   A_   )
def 							UpperCAmelCase   ( _lowercase,   _lowercase,   _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						accelerator.unwrap_model(A_   ).config
    _lowercase     			=						4 if args.gradient_checkpointing else 3
    _lowercase     			=						args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
    _lowercase     			=						24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
    _lowercase     			=						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))
    )
    _lowercase     			=						flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
    return tflops
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    model.eval()
    _lowercase     			=						[]
    for step, batch in enumerate(A_   ):
        with torch.no_grad():
            _lowercase     			=						model(A_,   labels=A_   )
        _lowercase     			=						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
    _lowercase     			=						torch.cat(A_   )
    _lowercase     			=						losses[: eval_dataloader.dataset.current_size].mean()
    try:
        _lowercase     			=						torch.exp(A_   )
    except OverflowError:
        _lowercase     			=						float('inf'   )
    return loss.item(), perplexity.item()


# Settings
lowerCamelCase					    =	HfArgumentParser(TrainingArguments)
lowerCamelCase					    =	parser.parse_args()

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

lowerCamelCase					    =	Namespace(**vars(args), **acc_state)
lowerCamelCase					    =	accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
lowerCamelCase    ,		lowerCamelCase					    =	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
lowerCamelCase					    =	AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
   model.gradient_checkpointing_enable()
lowerCamelCase					    =	AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
lowerCamelCase    ,		lowerCamelCase					    =	create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
lowerCamelCase					    =	AdamW(get_grouped_params(model, args), lr=args.learning_rate)
lowerCamelCase					    =	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`.
lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase					    =	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)
      lowerCamelCase					    =	os.path.basename(args.resume_from_checkpoint)
   else:
      # Get the most recent checkpoint
      lowerCamelCase					    =	[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)
      lowerCamelCase					    =	dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
   # Extract the step of the checkpoint to continue from there
   lowerCamelCase					    =	os.path.splitext(path)[0]
   lowerCamelCase					    =	int(training_difference.replace('''step_''', ''''''))

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


'''simple docstring'''





import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


UpperCAmelCase            = logging.get_logger(__name__)

if is_vision_available():
						import PIL.Image
						from PIL import Image
						from PIL.Image import Image as ImageType
else:
						UpperCAmelCase            = object

if is_torch_available():
						import torch

if is_soundfile_availble():
						import soundfile as sf

class     a :




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case							:  Any			=     value




				def __str__(				self						:					Optional[int]   ):
										return self.to_string()




				def 						__snake_case				(				self						:					Optional[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return self._value




				def 						__snake_case				(				self						:					List[Any]   ):
										logger.error(
										    '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''   )
										return str(self._value   )




class     a (					__magic_name__					,__magic_name__     ):




				def 						__snake_case				(				self						:					Optional[Any]   ):
										return self._value




				def 						__snake_case				(				self						:					str   ):
										return self._value




class     a (					__magic_name__					,__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

										if not is_vision_available():
																raise ImportError('''PIL must be installed in order to handle images.'''   )

										snake_case							:  Union[str, Any]			=     None
										snake_case							:  List[Any]			=     None
										snake_case							:  List[str]			=     None

										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  List[str]			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  Any			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  Union[str, Any]			=     value
										else:
																raise ValueError(F"""Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										from IPython.display import Image, display

										display(Image(self.to_string()   )   )




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._raw is not None:
																return self._raw

										if self._path is not None:
																snake_case							:  str			=     Image.open(self._path   )
																return self._raw




				def 						__snake_case				(				self						:					Union[str, Any]   ):
										if self._path is not None:
																return self._path

										if self._raw is not None:
																snake_case							:  int			=     tempfile.mkdtemp()
																snake_case							:  str			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )
																self._raw.save(self._path   )

																return self._path

										if self._tensor is not None:
																snake_case							:  Tuple			=     self._tensor.cpu().detach().numpy()

																# There is likely simpler than load into image into save
																snake_case							:  str			=     Image.fromarray((array * 2_55).astype(np.uinta   )   )

																snake_case							:  List[Any]			=     tempfile.mkdtemp()
																snake_case							:  Optional[Any]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.png'''   )

																img.save(self._path   )

																return self._path




class     a (					__magic_name__     ):




				def __init__(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int]=1_60_00   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )

										if not is_soundfile_availble():
																raise ImportError('''soundfile must be installed in order to handle audio.'''   )

										snake_case							:  List[Any]			=     None
										snake_case							:  int			=     None

										snake_case							:  Tuple			=     samplerate

										if isinstance(SCREAMING_SNAKE_CASE_,     (str, pathlib.Path)   ):
																snake_case							:  int			=     value
										elif isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  List[Any]			=     value
										else:
																raise ValueError(F"""Unsupported audio type: {type(SCREAMING_SNAKE_CASE_   )}"""   )




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None   ):
										from IPython.display import Audio, display

										display(Audio(self.to_string(),     rate=self.samplerate   )   )




				def 						__snake_case				(				self						:					List[str]   ):
										if self._tensor is not None:
																return self._tensor

										if self._path is not None:
																snake_case,       snake_case							:  Optional[Any]			=     sf.read(self._path   )
																snake_case							:  Any			=     torch.tensor(SCREAMING_SNAKE_CASE_   )
																return self._tensor




				def 						__snake_case				(				self						:					List[Any]   ):
										if self._path is not None:
																return self._path

										if self._tensor is not None:
																snake_case							:  Optional[Any]			=     tempfile.mkdtemp()
																snake_case							:  List[str]			=     os.path.join(SCREAMING_SNAKE_CASE_,     str(uuid.uuida()   ) + '''.wav'''   )
																sf.write(self._path,     self._tensor,     samplerate=self.samplerate   )
																return self._path




UpperCAmelCase            = {"text": AgentText, "image": AgentImage, "audio": AgentAudio}
UpperCAmelCase            = {str: AgentText}

if is_vision_available():
						UpperCAmelCase            = AgentImage

def   A			(							*A_				:       List[str]     ,				**A_				:       Any							):
						snake_case							:  str			=     [(arg.to_raw() if isinstance(A_     ,				A_							) else arg) for arg in args]
						snake_case							:  Dict			=     {k: (v.to_raw() if isinstance(A_     ,				A_							) else v) for k, v in kwargs.items()}
						return args, kwargs





def   A			(							A_				:       Union[str, Any]     ,				A_				:       Any=None							):
						if isinstance(A_     ,				A_							):
												snake_case							:  Union[str, Any]			=     {}
												for i, (k, v) in enumerate(outputs.items()							):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														snake_case							:  Optional[int]			=     AGENT_TYPE_MAPPING[output_types[i]](A_							)
																								else:
																														snake_case							:  List[str]			=     AgentType(A_							)

																		else:
																								# If the class does not have defined output, then we map according to the type
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(A_     ,				_k							):
																																				snake_case							:  Dict			=     _v(A_							)
																								if k not in decoded_outputs:
																														snake_case							:  List[str]			=     AgentType[v]

						elif isinstance(A_     ,				(list, tuple)							):
												snake_case							:  Union[str, Any]			=     type(A_							)()
												for i, v in enumerate(A_							):
																		if output_types is not None:
																								# If the class has defined outputs, we can map directly according to the class definition
																								if output_types[i] in AGENT_TYPE_MAPPING:
																														decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](A_							)							)
																								else:
																														decoded_outputs.append(AgentType(A_							)							)
																		else:
																								# If the class does not have defined output, then we map according to the type
																								snake_case							:  Union[str, Any]			=     False
																								for _k, _v in INSTANCE_TYPE_MAPPING.items():
																														if isinstance(A_     ,				_k							):
																																				decoded_outputs.append(_v(A_							)							)
																																				snake_case							:  Tuple			=     True

																								if not found:
																														decoded_outputs.append(AgentType(A_							)							)

						else:
												if output_types[0] in AGENT_TYPE_MAPPING:
																		# If the class has defined outputs, we can map directly according to the class definition
																		snake_case							:  List[str]			=     AGENT_TYPE_MAPPING[output_types[0]](A_							)

												else:
																		# If the class does not have defined output, then we map according to the type
																		for _k, _v in INSTANCE_TYPE_MAPPING.items():
																								if isinstance(A_     ,				_k							):
																														return _v(A_							)
																		return AgentType(A_							)

						return decoded_outputs

