


'''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  UpperCAmelCase (       lowerCAmelCase_):

 def __init__( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	Optional[Any]=False,					a_ :	Optional[Any]=1024,					a_ :	List[Any]=1024,					a_ :	Dict=3.6,					a_ :	Tuple=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 :	Any					):





    """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 		lowercase_						( self :	Optional[int],					a_ :	Optional[Any]=1000					):





    """simple docstring"""



    return ShufflerIterDataPipe(self,					buffer_size=lowerCamelCase_					)







def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Dict   )		->					Dict:





   '''simple docstring'''







   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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple   )		->					Dict:





   '''simple docstring'''







   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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]   )		->					Dict:





   '''simple docstring'''







   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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				List[str]   )		->					Optional[Any]:





   '''simple docstring'''







   logger.info(F'Step {step}: {metrics}'   )
   if accelerator.is_main_process:
      accelerator.log(a_    ,		a_   )



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Union[str, Any]    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				List[str]   )		->					int:





   '''simple docstring'''







   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 / (1_6.0 * config_model.n_layer * config_model.n_embd))
   )
   UpperCamelCase__ 						=						flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
   return tflops



def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				List[Any]   )		->					Dict:





   '''simple docstring'''







   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
__lowercase:		Optional[int]							=     HfArgumentParser(TrainingArguments)
__lowercase:		Union[str, Any]							=     parser.parse_args()

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

# Clone model repository
if accelerator.is_main_process:
       __lowercase:		Optional[Any]							=     Repository(args.save_dir, clone_from=args.model_ckpt)

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

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

# Prepare the optimizer and learning rate scheduler
__lowercase:		Union[str, Any]							=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
__lowercase:		Any							=     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        SCREAMING_SNAKE_CASE__(		)		->					int:





   '''simple docstring'''







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


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

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



'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast







@require_vision
class  UpperCAmelCase (       unittest.TestCase):

 def 		lowercase_						( self :	Optional[Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						tempfile.mkdtemp()

    UpperCamelCase__ 						=						CLIPImageProcessor()
    UpperCamelCase__ 						=						BertTokenizer.from_pretrained(
        "hf-internal-testing/tiny-random-BertModel",					model_input_names=["input_ids", "attention_mask"]					)

    UpperCamelCase__ 						=						GitProcessor(a_,					a_					)

    processor.save_pretrained(self.tmpdirname					)

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	str					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).tokenizer

 def 		lowercase_						( self :	Union[str, Any],					**a_ :	List[str]					):





    """simple docstring"""



    return AutoProcessor.from_pretrained(self.tmpdirname,					**a_					).image_processor

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    shutil.rmtree(self.tmpdirname					)

 def 		lowercase_						( self :	str					):





    """simple docstring"""



    UpperCamelCase__ 						=						[np.random.randint(255,					size=(3, 30, 400),					dtype=np.uinta					)]

    UpperCamelCase__ 						=						[Image.fromarray(np.moveaxis(a_,					0,					-1					)					) for x in image_inputs]

    return image_inputs

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						GitProcessor(tokenizer=self.get_tokenizer(),					image_processor=self.get_image_processor()					)
    processor.save_pretrained(self.tmpdirname					)

    UpperCamelCase__ 						=						self.get_tokenizer(bos_token="(BOS)",					eos_token="(EOS)"					)
    UpperCamelCase__ 						=						self.get_image_processor(do_normalize=a_,					padding_value=1.0					)

    UpperCamelCase__ 						=						GitProcessor.from_pretrained(
        self.tmpdirname,					bos_token="(BOS)",					eos_token="(EOS)",					do_normalize=a_,					padding_value=1.0					)

    self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()					)
    self.assertIsInstance(processor.tokenizer,					a_					)

    self.assertEqual(processor.image_processor.to_json_string(),					image_processor_add_kwargs.to_json_string()					)
    self.assertIsInstance(processor.image_processor,					a_					)

 def 		lowercase_						( self :	int					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						image_processor(a_,					return_tensors="np"					)
    UpperCamelCase__ 						=						processor(images=a_,					return_tensors="np"					)

    for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum(),					input_processor[key].sum(),					delta=1e-2					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"

    UpperCamelCase__ 						=						processor(text=a_					)

    UpperCamelCase__ 						=						tokenizer(a_,					return_token_type_ids=a_					)

    for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key],					encoded_processor[key]					)

 def 		lowercase_						( self :	Any					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"
    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						processor(text=a_,					images=a_					)

    self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)

    # test if it raises when no input is passed
    with pytest.raises(a_					):
       processor()

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    UpperCamelCase__ 						=						processor.batch_decode(a_					)
    UpperCamelCase__ 						=						tokenizer.batch_decode(a_					)

    self.assertListEqual(a_,					a_					)

 def 		lowercase_						( self :	Union[str, Any]					):





    """simple docstring"""



    UpperCamelCase__ 						=						self.get_image_processor()
    UpperCamelCase__ 						=						self.get_tokenizer()

    UpperCamelCase__ 						=						GitProcessor(tokenizer=a_,					image_processor=a_					)

    UpperCamelCase__ 						=						"lower newer"
    UpperCamelCase__ 						=						self.prepare_image_inputs()

    UpperCamelCase__ 						=						processor(text=a_,					images=a_					)

    # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
    self.assertListEqual(list(inputs.keys()					),					["input_ids", "attention_mask", "pixel_values"]					)