
"""simple docstring"""







import argparse
import collections
import importlib.util
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
SCREAMING_SNAKE_CASE_     				=   'src/diffusers'
SCREAMING_SNAKE_CASE_     				=   'docs/source/en'
SCREAMING_SNAKE_CASE_     				=   '.'
def 			__UpperCAmelCase		(		a_			:		Optional[Any]    ,  a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
   with open(a_    ,  "r"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
      _UpperCAmelCase   	=					f.readlines()
   # Find the start prompt.
   _UpperCAmelCase   	=					0
   while not lines[start_index].startswith(a_				):
      start_index += 1
   start_index += 1

   _UpperCAmelCase   	=					start_index
   while not lines[end_index].startswith(a_				):
      end_index += 1
   end_index -= 1

   while len(lines[start_index]				) <= 1:
      start_index += 1
   while len(lines[end_index]				) <= 1:
      end_index -= 1
   end_index += 1
   return "".join(lines[start_index:end_index]				), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
SCREAMING_SNAKE_CASE_     				=   'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE_     				=   re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE_     				=   re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE_     				=   re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the diffusers module imported is the one in the repo.
SCREAMING_SNAKE_CASE_     				=   importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(TRANSFORMERS_PATH, '__init__.py'),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
SCREAMING_SNAKE_CASE_     				=   spec.loader.load_module()
def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
   _UpperCAmelCase   	=					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,  a_				)
   return [m.group(0				) for m in matches]
def 			__UpperCAmelCase		(		a_			:		Any    ,  a_			:		List[Any]				)     ->       Optional[int]:
   _UpperCAmelCase   	=					2 if text == "✅" or text == "❌" else len(a_				)
   _UpperCAmelCase   	=					(width - text_length) // 2
   _UpperCAmelCase   	=					width - text_length - left_indent
   return " " * left_indent + text + " " * right_indent
def 			__UpperCAmelCase		(		)     ->       List[Any]:
   _UpperCAmelCase   	=					diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
   _UpperCAmelCase   	=					{
       name: config_mapping_names[code]
       for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
       if code in config_mapping_names
   }
   _UpperCAmelCase   	=					{name: config.replace("ConfigMixin"    ,  ""				) for name, config in model_name_to_config.items()}

   # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)
   _UpperCAmelCase   	=					collections.defaultdict(a_				)

   # Let's lookup through all diffusers object (once).
   for attr_name in dir(a_				):
      _UpperCAmelCase   	=					None
      if attr_name.endswith("Tokenizer"				):
         _UpperCAmelCase   	=					slow_tokenizers
         _UpperCAmelCase   	=					attr_name[:-9]
      elif attr_name.endswith("TokenizerFast"				):
         _UpperCAmelCase   	=					fast_tokenizers
         _UpperCAmelCase   	=					attr_name[:-13]
      elif _re_tf_models.match(a_				) is not None:
         _UpperCAmelCase   	=					tf_models
         _UpperCAmelCase   	=					_re_tf_models.match(a_				).groups()[0]
      elif _re_flax_models.match(a_				) is not None:
         _UpperCAmelCase   	=					flax_models
         _UpperCAmelCase   	=					_re_flax_models.match(a_				).groups()[0]
      elif _re_pt_models.match(a_				) is not None:
         _UpperCAmelCase   	=					pt_models
         _UpperCAmelCase   	=					_re_pt_models.match(a_				).groups()[0]

      if lookup_dict is not None:
         while len(a_				) > 0:
            if attr_name in model_name_to_prefix.values():
               _UpperCAmelCase   	=					True
               break
            # Try again after removing the last word in the name
            _UpperCAmelCase   	=					"".join(camel_case_split(a_				)[:-1]				)

    # Let's build that table!
   _UpperCAmelCase   	=					list(model_name_to_config.keys()				)
   model_names.sort(key=str.lower				)
   _UpperCAmelCase   	=					["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
   # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
   _UpperCAmelCase   	=					[len(a_				) + 2 for c in columns]
   _UpperCAmelCase   	=					max([len(a_				) for name in model_names]				) + 2

   # Build the table per se
   _UpperCAmelCase   	=					"|" + "|".join([_center_text(a_    ,  a_				) for c, w in zip(a_    ,  a_				)]				) + "|\n"
   # Use ":-----:" format to center-aligned table cell texts
   table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]				) + "|\n"

   _UpperCAmelCase   	=					{True: "✅", False: "❌"}
   for name in model_names:
      _UpperCAmelCase   	=					model_name_to_prefix[name]
      _UpperCAmelCase   	=					[
          name,
          check[slow_tokenizers[prefix]],
          check[fast_tokenizers[prefix]],
          check[pt_models[prefix]],
          check[tf_models[prefix]],
          check[flax_models[prefix]],
      ]
      table += "|" + "|".join([_center_text(a_    ,  a_				) for l, w in zip(a_    ,  a_				)]				) + "|\n"
   return table
def 			__UpperCAmelCase		(		a_			:		Union[str, Any]=False				)     ->       int:
   _UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase   	=					_find_text_in_file(
       filename=os.path.join(a_    ,  "index.mdx"				)    ,  start_prompt="<!--This table is updated automatically from the auto modules"    ,  end_prompt="<!-- End table-->"    ,  )
   _UpperCAmelCase   	=					get_model_table_from_auto_modules()

   if current_table != new_table:
      if overwrite:
         with open(os.path.join(a_    ,  "index.mdx"				)    ,  "w"    ,  encoding="utf-8"    ,  newline="\n"				) as f:
            f.writelines(lines[:start_index] + [new_table] + lines[end_index:]				)
      else:
         raise ValueError(
             "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."				)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_     				=   argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  SCREAMING_SNAKE_CASE_     				=   parser.parse_args()

  check_model_table(args.fix_and_overwrite)



"""simple docstring"""







import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed






class     __a			(					lowerCAmelCase_			):







       def __init__(					self							,lowerCamelCase_							,lowerCamelCase_							,lowerCamelCase_=False							,lowerCamelCase_=1024							,lowerCamelCase_=1024							,lowerCamelCase_=3.6							,lowerCamelCase_=False							,):



          '''simple docstring'''

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

          if self.tokenized:
             _UpperCAmelCase   	=					seq_length * num_of_sequences
             _UpperCAmelCase   	=					"input_ids"
          else:
             _UpperCAmelCase   	=					seq_length * chars_per_token * num_of_sequences
             _UpperCAmelCase   	=					"content"







       def __iter__(					self		):



          '''simple docstring'''

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







       def    _snake_case			(					self							,lowerCamelCase_=1000		):



          '''simple docstring'''

          return ShufflerIterDataPipe(self							,buffer_size=lowerCamelCase_		)



def 			__UpperCAmelCase		(		a_			:		Dict				)     ->       Dict:
   _UpperCAmelCase   	=					args.model_ckpt.split("/"				)[-1]
   _UpperCAmelCase   	=					logging.getLogger(__name__				)
   _UpperCAmelCase   	=					Path(args.save_dir				) / "log/"
   log_dir.mkdir(exist_ok=a_				)
   _UpperCAmelCase   	=					F'''debug_{accelerator.process_index}.log'''
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"    ,  datefmt="%m/%d/%Y %H:%M:%S"    ,  level=logging.INFO    ,  handlers=[logging.FileHandler(log_dir / filename				), logging.StreamHandler()]    ,  )
   if accelerator.is_main_process:  # we only want to setup logging once
      accelerator.init_trackers(a_    ,  vars(a_				)				)
      _UpperCAmelCase   	=					accelerator.trackers[0].run.name
      logger.setLevel(logging.INFO				)
      datasets.utils.logging.set_verbosity_info()
      transformers.utils.logging.set_verbosity_info()
   else:
      _UpperCAmelCase   	=					""
      logger.setLevel(logging.ERROR				)
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()
   return logger, run_name
def 			__UpperCAmelCase		(		a_			:		Tuple				)     ->       Dict:
   _UpperCAmelCase   	=					{"streaming": True}
   _UpperCAmelCase   	=					load_dataset(args.dataset_name_train    ,  split="train"    ,  **a_				)
   _UpperCAmelCase   	=					train_data.shuffle(buffer_size=args.shuffle_buffer    ,  seed=args.seed				)
   _UpperCAmelCase   	=					load_dataset(args.dataset_name_valid    ,  split="train"    ,  **a_				)
   _UpperCAmelCase   	=					ConstantLengthDataset(
       a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
   _UpperCAmelCase   	=					ConstantLengthDataset(
       a_    ,  a_    ,  infinite=a_    ,  seq_length=args.seq_length    ,  tokenized=args.tokenized				)
   _UpperCAmelCase   	=					train_dataset.shuffle(buffer_size=args.shuffle_buffer				)
   _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.train_batch_size    ,  shuffle=a_				)
   _UpperCAmelCase   	=					DataLoader(a_    ,  batch_size=args.valid_batch_size				)
   return train_dataloader, eval_dataloader
def 			__UpperCAmelCase		(		a_			:		List[Any]    ,  a_			:		List[Any]    ,  a_			:		Optional[int]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]				)     ->       Dict:
   _UpperCAmelCase	,							_UpperCAmelCase   	=					[], []
   for n, p in model.named_parameters():
      if any(nd in n for nd in no_decay				):
         params_without_wd.append(a_				)
      else:
         params_with_wd.append(a_				)
   return [
       {"params": params_with_wd, "weight_decay": args.weight_decay},
       {"params": params_without_wd, "weight_decay": 0.0},
   ]
def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[str]				)     ->       Optional[Any]:
   logger.info(F'''Step {step}: {metrics}'''				)
   if accelerator.is_main_process:
      accelerator.log(a_    ,  a_				)
def 			__UpperCAmelCase		(		a_			:		Union[str, Any]    ,  a_			:		List[Any]    ,  a_			:		List[str]				)     ->       int:
   # TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
   _UpperCAmelCase   	=					accelerator.unwrap_model(a_				).config
   _UpperCAmelCase   	=					4 if args.gradient_checkpointing else 3
   _UpperCAmelCase   	=					args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
   _UpperCAmelCase   	=					24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
   _UpperCAmelCase   	=					factor * (
       1.0
       + (args.seq_length / (6.0 * config_model.n_embd))
       + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
   )
   _UpperCAmelCase   	=					flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
   return tflops
def 			__UpperCAmelCase		(		a_			:		List[Any]				)     ->       Dict:
   model.eval()
   _UpperCAmelCase   	=					[]
   for step, batch in enumerate(a_				):
      with torch.no_grad():
         _UpperCAmelCase   	=					model(a_    ,  labels=a_				)
      _UpperCAmelCase   	=					outputs.loss.repeat(args.valid_batch_size				)
      losses.append(accelerator.gather(a_				)				)
      if args.max_eval_steps > 0 and step >= args.max_eval_steps:
         break
   _UpperCAmelCase   	=					torch.cat(a_				)
   _UpperCAmelCase   	=					losses[: eval_dataloader.dataset.current_size].mean()
   try:
      _UpperCAmelCase   	=					torch.exp(a_				)
   except OverflowError:
      _UpperCAmelCase   	=					float("inf"				)
   return loss.item(), perplexity.item()


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

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

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

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

# Logging
SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
  hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
SCREAMING_SNAKE_CASE_     				=   AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
  model.gradient_checkpointing_enable()
SCREAMING_SNAKE_CASE_     				=   AutoTokenizer.from_pretrained(args.save_dir)

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

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE_     				=   AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE_     				=   get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)
def 			__UpperCAmelCase		(		)     ->       int:
   return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
  if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
    accelerator.print(f'''Resumed from checkpoint: {args.resume_from_checkpoint}''')
    accelerator.load_state(args.resume_from_checkpoint)
    SCREAMING_SNAKE_CASE_     				=   os.path.basename(args.resume_from_checkpoint)
  else:
    # Get the most recent checkpoint
    SCREAMING_SNAKE_CASE_     				=   [f.name for f in os.scandir(args.save_dir) if f.is_dir() and 'step' in str(f)]
    dirs.sort(key=os.path.getctime)
    SCREAMING_SNAKE_CASE_     				=   dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  # Extract the step of the checkpoint to continue from there
  SCREAMING_SNAKE_CASE_     				=   os.path.splitext(path)[0]
  SCREAMING_SNAKE_CASE_     				=   int(training_difference.replace('step_', ''))

# Train model
model.train()
SCREAMING_SNAKE_CASE_     				=   0
SCREAMING_SNAKE_CASE_     				=   time.time()
SCREAMING_SNAKE_CASE_     				=   0
for step, batch in enumerate(train_dataloader, start=1):
  if args.resume_from_checkpoint and step < resume_step:
    continue  # we need to skip steps until we reach the resumed step
  SCREAMING_SNAKE_CASE_     				=   model(batch, labels=batch, use_cache=False).loss
  SCREAMING_SNAKE_CASE_     				=   accelerator.gather(loss.repeat(args.train_batch_size)).mean()
  loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
  log_metrics(step, {'samples': step * samples_per_step, 'loss_per_step/train': loss.item()})
  SCREAMING_SNAKE_CASE_     				=   loss / args.gradient_accumulation_steps
  if step % args.gradient_accumulation_steps != 0:
    # Prevent backward from doing gradient all_reduce in every step
    if accelerator.distributed_type == DistributedType.MULTI_GPU:
      with model.no_sync():
        accelerator.backward(loss)
    else:
      accelerator.backward(loss)
  else:
    SCREAMING_SNAKE_CASE_     				=   get_lr()
    accelerator.backward(loss)
    accelerator.clip_grad_norm_(model.parameters(), 1.0)
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()
    SCREAMING_SNAKE_CASE_     				=   time.time() - t_start
    SCREAMING_SNAKE_CASE_     				=   compute_tflops(elapsed_time, accelerator, args)
    log_metrics(
        step,
        {
            'steps': completed_steps,
            'loss/train': loss_tracking,
            'lr': lr,
            'tflops': tflops,
            'time_per_iteration': elapsed_time,
        },
    )
    SCREAMING_SNAKE_CASE_     				=   time.time()
    SCREAMING_SNAKE_CASE_     				=   0
    completed_steps += 1
  if step % args.save_checkpoint_steps == 0:
    logger.info('Evaluating and saving model checkpoint')
    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
    log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
    accelerator.wait_for_everyone()
    SCREAMING_SNAKE_CASE_     				=   os.path.join(args.save_dir, f'''step_{step}''')
    accelerator.save_state(save_dir)
    if accelerator.is_main_process:
      hf_repo.push_to_hub(commit_message=f'''step {step}''')
    model.train()
  if completed_steps >= args.max_train_steps:
    break

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_     				=   evaluate(args)
log_metrics(step, {'loss/eval': eval_loss, 'perplexity': perplexity})
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE_     				=   accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
SCREAMING_SNAKE_CASE_     				=   os.path.join(args.save_dir, f'''step_{step}''')
accelerator.save_state(save_dir)
if accelerator.is_main_process:
  hf_repo.push_to_hub(commit_message='final model')

