
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__		(     __SCREAMING_SNAKE_CASE):
      def __init__(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__=False   ,  __magic_name__=1_0_2_4   ,  __magic_name__=1_0_2_4   ,  __magic_name__=3.6   ,  __magic_name__=False   ,  ):
       lowerCamelCase		:     Union[str, Any]          =				tokenizer
       lowerCamelCase		:     List[Any]          =				tokenizer.bos_token_id
       lowerCamelCase		:     Any          =				dataset
       lowerCamelCase		:     List[str]          =				seq_length
       lowerCamelCase		:     List[Any]          =				0
       lowerCamelCase		:     Dict          =				infinite
       lowerCamelCase		:     Optional[Any]          =				0
       lowerCamelCase		:     int          =				tokenized

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






      def 						UpperCamelCase__							(				self   ,  __magic_name__=1_0_0_0				):
       return ShufflerIterDataPipe(self   ,  buffer_size=__magic_name__				)





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





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





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							):
 lowerCamelCase		:     Any          =				[], []
 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    _a (						lowerCamelCase,     lowerCamelCase							):
 logger.info(F'''Step {step}: {metrics}'''							)
 if accelerator.is_main_process:
  accelerator.log(lowerCamelCase,     lowerCamelCase							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
 lowerCamelCase		:     Union[str, Any]          =				accelerator.unwrap_model(lowerCamelCase							).config
 lowerCamelCase		:     Any          =				4 if args.gradient_checkpointing else 3
 lowerCamelCase		:     Dict          =				args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
 lowerCamelCase		:     List[Any]          =				24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
 lowerCamelCase		:     Tuple          =				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))
 )
 lowerCamelCase		:     int          =				flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
 return tflops





def    _a (						lowerCamelCase							):
 model.eval()
 lowerCamelCase		:     int          =				[]
 for step, batch in enumerate(lowerCamelCase							):
  with torch.no_grad():
   lowerCamelCase		:     Optional[int]          =				model(lowerCamelCase,     labels=lowerCamelCase							)
  lowerCamelCase		:     Union[str, 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
 lowerCamelCase		:     str          =				torch.cat(lowerCamelCase							)
 lowerCamelCase		:     Optional[int]          =				losses[: eval_dataloader.dataset.current_size].mean()
 try:
  lowerCamelCase		:     Union[str, Any]          =				torch.exp(lowerCamelCase							)
 except OverflowError:
  lowerCamelCase		:     Dict          =				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    _a (						):
 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""")






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase        ={
    """configuration_tvlt""": ["""TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TvltConfig"""],
    """feature_extraction_tvlt""": ["""TvltFeatureExtractor"""],
    """processing_tvlt""": ["""TvltProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =[
          """TVLT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TvltModel""",
          """TvltForPreTraining""",
          """TvltForAudioVisualClassification""",
          """TvltPreTrainedModel""",
      ]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase        =["""TvltImageProcessor"""]


if TYPE_CHECKING:
      from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
      from .processing_tvlt import TvltProcessor
      from .feature_extraction_tvlt import TvltFeatureExtractor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tvlt import (
                TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TvltForAudioVisualClassification,
                TvltForPreTraining,
                TvltModel,
                TvltPreTrainedModel,
            )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_tvlt import TvltImageProcessor


else:
      import sys

      _lowerCamelCase        =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




