
"""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      lowerCAmelCase_						(lowerCAmelCase__  ):


				"""simple docstring"""



				def __init__(self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=False       ,	SCREAMING_SNAKE_CASE__=10_24       ,	SCREAMING_SNAKE_CASE__=10_24       ,	SCREAMING_SNAKE_CASE__=3.6       ,	SCREAMING_SNAKE_CASE__=False       ,	) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	int									=       tokenizer
											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       tokenizer.bos_token_id
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       dataset
											SCREAMING_SNAKE_CASE__						:	List[str]									=       seq_length
											SCREAMING_SNAKE_CASE__						:	Any									=       0
											SCREAMING_SNAKE_CASE__						:	int									=       infinite
											SCREAMING_SNAKE_CASE__						:	Dict									=       0
											SCREAMING_SNAKE_CASE__						:	Any									=       tokenized

											if self.tokenized:
																		SCREAMING_SNAKE_CASE__						:	str									=       seq_length * num_of_sequences
																		SCREAMING_SNAKE_CASE__						:	str									=       """input_ids"""
											else:
																		SCREAMING_SNAKE_CASE__						:	Dict									=       seq_length * chars_per_token * num_of_sequences
																		SCREAMING_SNAKE_CASE__						:	Any									=       """content"""



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       iter(self.dataset   )
											SCREAMING_SNAKE_CASE__						:	Dict									=       True
											while more_examples:
																		SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	List[str]									=       [], 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:
																																							SCREAMING_SNAKE_CASE__						:	Tuple									=       iter(self.dataset   )
																																							self.epoch += 1
																																							logger.info(F'''Dataset epoch: {self.epoch}'''   )
																																else:
																																							SCREAMING_SNAKE_CASE__						:	Dict									=       False
																																							break
																		if self.tokenized:
																									SCREAMING_SNAKE_CASE__						:	Dict									=       buffer
																		else:
																									SCREAMING_SNAKE_CASE__						:	Tuple									=       self.tokenizer(__lowerCAmelCase       ,	truncation=__lowerCAmelCase   )["""input_ids"""]
																		SCREAMING_SNAKE_CASE__						:	int									=       []
																		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   ):
																									SCREAMING_SNAKE_CASE__						:	str									=       all_token_ids[i : i + self.seq_length]
																									if len(__lowerCAmelCase   ) == self.seq_length:
																																self.current_size += 1
																																yield torch.tensor(__lowerCAmelCase   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__=10_00   ) ->		Dict:



											"""simple docstring"""




											return ShufflerIterDataPipe(self       ,	buffer_size=__lowerCAmelCase   )






def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	Any									=       args.model_ckpt.split("""/"""						)[-1]
							SCREAMING_SNAKE_CASE__						:	Tuple									=       logging.getLogger(__name__						)
							SCREAMING_SNAKE_CASE__						:	Any									=       Path(args.save_dir						) / """log/"""
							log_dir.mkdir(exist_ok=_A						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       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						)						)
														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__						:	Optional[int]									=       """"""
														logger.setLevel(logging.ERROR						)
														datasets.utils.logging.set_verbosity_error()
														transformers.utils.logging.set_verbosity_error()
							return logger, run_name

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

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]						):
							SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Dict									=       [], []
							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 lowercase_  (       _snake_case       ,_snake_case						):
							logger.info(f'''Step {step}: {metrics}'''						)
							if accelerator.is_main_process:
														accelerator.log(_A       ,_A						)

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       accelerator.unwrap_model(_A						).config
							SCREAMING_SNAKE_CASE__						:	Tuple									=       4 if args.gradient_checkpointing else 3
							SCREAMING_SNAKE_CASE__						:	List[str]									=       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
							SCREAMING_SNAKE_CASE__						:	Dict									=       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       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))
							)
							SCREAMING_SNAKE_CASE__						:	int									=       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
							return tflops

def lowercase_  (       _snake_case						):
							model.eval()
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       []
							for step, batch in enumerate(_A						):
														with torch.no_grad():
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       model(_A       ,labels=_A						)
														SCREAMING_SNAKE_CASE__						:	Any									=       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
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.cat(_A						)
							SCREAMING_SNAKE_CASE__						:	int									=       losses[: eval_dataloader.dataset.current_size].mean()
							try:
														SCREAMING_SNAKE_CASE__						:	Any									=       torch.exp(_A						)
							except OverflowError:
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       float("""inf"""						)
							return loss.item(), perplexity.item()


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

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

UpperCAmelCase__				:   Dict   							=			Namespace(**vars(args), **acc_state)
UpperCAmelCase__				:   int   							=			accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

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

# Load dataset and dataloader
UpperCAmelCase__							,    UpperCAmelCase__				:   Dict   							=			create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCAmelCase__				:   int   							=			AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCAmelCase__				:   Union[str, 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 lowercase_  (       ):
							return optimizer.param_groups[0]["lr"]


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

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

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
					import sqlitea

					import sqlalchemy


UpperCAmelCase__				:   Union[str, Any]   							=			datasets.utils.logging.get_logger(__name__)



@dataclass
class      lowerCAmelCase_						(datasets.BuilderConfig  ):


				"""simple docstring"""



				__UpperCamelCase				:						Union[str, "sqlalchemy.sql.Selectable"] 	=       None
				__UpperCamelCase				:						Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] 	=       None
				__UpperCamelCase				:						Optional[Union[str, List[str]]] 	=       None
				__UpperCamelCase				:						bool 	=       True
				__UpperCamelCase				:						Optional[Union[List, Tuple, Dict]] 	=       None
				__UpperCamelCase				:						Optional[Union[List, Dict]] 	=       None
				__UpperCamelCase				:						Optional[List[str]] 	=       None
				__UpperCamelCase				:						Optional[int] 	=       10000
				__UpperCamelCase				:						Optional[datasets.Features] 	=       None



				def      __magic_name__   (self   ) ->		Dict:



											"""simple docstring"""




											if self.sql is None:
																		raise ValueError("""sql must be specified"""   )
											if self.con is None:
																		raise ValueError("""con must be specified"""   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__ = None       ,	) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       config_kwargs.copy()
											# We need to stringify the Selectable object to make its hash deterministic

											# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
											SCREAMING_SNAKE_CASE__						:	Dict									=       config_kwargs["""sql"""]
											if not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																									import sqlalchemy

																									if isinstance(SCREAMING_SNAKE_CASE__       ,	sqlalchemy.sql.Selectable   ):
																																SCREAMING_SNAKE_CASE__						:	str									=       sqlalchemy.create_engine(config_kwargs["""con"""].split("""://"""   )[0] + """://"""   )
																																SCREAMING_SNAKE_CASE__						:	Tuple									=       str(sql.compile(dialect=engine.dialect   )   )
																																SCREAMING_SNAKE_CASE__						:	Any									=       sql_str
																									else:
																																raise TypeError(
																																    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(SCREAMING_SNAKE_CASE__   )}: {sql}'''   )
																		else:
																									raise TypeError(
																									    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(SCREAMING_SNAKE_CASE__   )}: {sql}'''   )
											SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       config_kwargs["""con"""]
											if not isinstance(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       id(SCREAMING_SNAKE_CASE__   )
																		logger.info(
																		    F'''SQL connection \'con\' of type {type(SCREAMING_SNAKE_CASE__   )} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''   )

											return super().create_config_id(SCREAMING_SNAKE_CASE__       ,	custom_features=SCREAMING_SNAKE_CASE__   )



				@property
				def      __magic_name__   (self   ) ->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	Optional[int]									=       {
											    """index_col""": self.index_col,
											    """columns""": self.columns,
											    """params""": self.params,
											    """coerce_float""": self.coerce_float,
											    """parse_dates""": self.parse_dates,
											}
											return pd_read_sql_kwargs






class      lowerCAmelCase_						(datasets.ArrowBasedBuilder  ):


				"""simple docstring"""



				__UpperCamelCase				:						List[str] 	=       SqlConfig



				def      __magic_name__   (self   ) ->		List[Any]:



											"""simple docstring"""




											return datasets.DatasetInfo(features=self.config.features   )



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		List[str]:



											"""simple docstring"""




											return [datasets.SplitGenerator(name=datasets.Split.TRAIN       ,	gen_kwargs={}   )]



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		pa.Table:



											"""simple docstring"""




											if self.config.features is not None:
																		SCREAMING_SNAKE_CASE__						:	List[str]									=       self.config.features.arrow_schema
																		if all(not require_storage_cast(SCREAMING_SNAKE_CASE__   ) for feature in self.config.features.values()   ):
																									# cheaper cast
																									SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       pa.Table.from_arrays([pa_table[field.name] for field in schema]       ,	schema=SCREAMING_SNAKE_CASE__   )
																		else:
																									# more expensive cast; allows str <-> int/float or str to Audio for example
																									SCREAMING_SNAKE_CASE__						:	Optional[int]									=       table_cast(SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__   )
											return pa_table



				def      __magic_name__   (self   ) ->		Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:	List[Any]									=       self.config.chunksize
											SCREAMING_SNAKE_CASE__						:	Any									=       pd.read_sql(
											    self.config.sql       ,	self.config.con       ,	chunksize=SCREAMING_SNAKE_CASE__       ,	**self.config.pd_read_sql_kwargs   )
											SCREAMING_SNAKE_CASE__						:	str									=       [sql_reader] if chunksize is None else sql_reader
											for chunk_idx, df in enumerate(SCREAMING_SNAKE_CASE__   ):
																		SCREAMING_SNAKE_CASE__						:	int									=       pa.Table.from_pandas(SCREAMING_SNAKE_CASE__   )
																		yield chunk_idx, self._cast_table(SCREAMING_SNAKE_CASE__   )




