



import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import (
	    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)

if is_tf_available() and is_tensorflow_probability_available():
	import tensorflow as tf
	import tensorflow_probability as tfp

	from ..models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)



class 							A					( lowerCAmelCase__							):
					'''simple docstring'''






					def __call__(self					:		Union[str, Any]   ,      _UpperCAmelCase					:		Optional[Any]=None   ,      _UpperCAmelCase					:		int=None   ,      **_UpperCAmelCase					:		str	)		->     Optional[Any]:




							"""simple docstring"""


							requires_backends(self   ,      """pandas"""	)
							import pandas as pd

							if table is None:
									raise ValueError("""Keyword argument `table` cannot be None."""	)
							elif query is None:
									if isinstance(_lowerCamelCase   ,      _lowerCamelCase	) and table.get("""query"""	) is not None and table.get("""table"""	) is not None:
											lowercase__      =	[table]
									elif isinstance(_lowerCamelCase   ,      _lowerCamelCase	) and len(_lowerCamelCase	) > 0:
											if not all(isinstance(_lowerCamelCase   ,      _lowerCamelCase	) for d in table	):
													raise ValueError(
													    f'''Keyword argument `table` should be a list of dict, but is {(type(_lowerCamelCase	) for d in table)}'''	)

											if table[0].get("""query"""	) is not None and table[0].get("""table"""	) is not None:
													lowercase__      =	table
											else:
													raise ValueError(
													    """If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"""
													    f''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'''	)
									elif Dataset is not None and isinstance(_lowerCamelCase   ,      _lowerCamelCase	) or isinstance(_lowerCamelCase   ,      types.GeneratorType	):
											return table
									else:
											raise ValueError(
											    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
											    f'''is {type(_lowerCamelCase	)})'''	)
							else:
									lowercase__      =	[{"""table""": table, """query""": query}]

							for tqa_pipeline_input in tqa_pipeline_inputs:
									if not isinstance(tqa_pipeline_input["""table"""]   ,      pd.DataFrame	):
											if tqa_pipeline_input["table"] is None:
													raise ValueError("""Table cannot be None."""	)

											lowercase__      =	pd.DataFrame(tqa_pipeline_input["""table"""]	)

							return tqa_pipeline_inputs






@add_end_docstrings(lowerCAmelCase__							)
class 							A					( lowerCAmelCase__							):
					'''simple docstring'''






					A__    =				'''table,query'''

					def __init__(self					:		Union[str, Any]   ,      _UpperCAmelCase					:		int=TableQuestionAnsweringArgumentHandler()   ,      *_UpperCAmelCase					:		List[Any]   ,      **_UpperCAmelCase					:		List[Any]	)		->     Dict:




							"""simple docstring"""


							super().__init__(*_lowerCamelCase   ,      **_lowerCamelCase	)
							lowercase__      =	args_parser

							self.check_model_type(
							    dict(
							        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
							        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()	)
							    if self.framework == """tf"""
							    else dict(
							        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()	)	)

							lowercase__      =	bool(getattr(self.model.config   ,      """aggregation_labels"""   ,      _lowerCamelCase	)	) and bool(
							    getattr(self.model.config   ,      """num_aggregation_labels"""   ,      _lowerCamelCase	)	)
							lowercase__      =	"""tapas""" if hasattr(self.model.config   ,      """aggregation_labels"""	) else None

					def 						lowerCamelCase__  (self					:		Dict   ,      **_UpperCAmelCase					:		Any	)		->     int:




							"""simple docstring"""


							return self.model(**_lowerCamelCase	)

					def 						lowerCamelCase__  (self					:		List[str]   ,      **_UpperCAmelCase					:		List[str]	)		->     Tuple:




							"""simple docstring"""


							if self.framework == "pt":
									lowercase__      =	[]
									lowercase__      =	[]
									lowercase__      =	None
									lowercase__      =	inputs["""input_ids"""].shape[0]

									lowercase__      =	inputs["""input_ids"""].to(self.device	)
									lowercase__      =	inputs["""attention_mask"""].to(self.device	)
									lowercase__      =	inputs["""token_type_ids"""].to(self.device	)
									lowercase__      =	None

									for index in range(_lowerCamelCase	):
											# If sequences have already been processed, the token type IDs will be created according to the previous
											# answer.
											if prev_answers is not None:
													lowercase__      =	token_type_ids_example[:, 3]  # shape (seq_len,)
													lowercase__      =	np.zeros_like(prev_labels_example.cpu().numpy()	)  # shape (seq_len,)

													lowercase__      =	token_type_ids[index]  # shape (seq_len, 7)
													for i in range(model_labels.shape[0]	):
															lowercase__      =	token_type_ids_example[:, 0].tolist()[i]
															lowercase__      =	token_type_ids_example[:, 1].tolist()[i] - 1
															lowercase__      =	token_type_ids_example[:, 2].tolist()[i] - 1

															if row_id >= 0 and col_id >= 0 and segment_id == 1:
																	lowercase__      =	int(prev_answers[(col_id, row_id)]	)

													lowercase__      =	torch.from_numpy(_lowerCamelCase	).type(torch.long	).to(self.device	)

											lowercase__      =	input_ids[index]
											lowercase__      =	attention_mask[index]  # shape (seq_len,)
											lowercase__      =	token_type_ids[index]  # shape (seq_len, 7)
											lowercase__      =	self.model(
											    input_ids=input_ids_example.unsqueeze(0	)   ,      attention_mask=attention_mask_example.unsqueeze(0	)   ,      token_type_ids=token_type_ids_example.unsqueeze(0	)   ,      )
											lowercase__      =	outputs.logits

											if self.aggregate:
													all_aggregations.append(outputs.logits_aggregation	)

											all_logits.append(_lowerCamelCase	)

											lowercase__      =	torch.distributions.Bernoulli(logits=_lowerCamelCase	)
											lowercase__      =	dist_per_token.probs * attention_mask_example.type(torch.floataa	).to(
											    dist_per_token.probs.device	)

											lowercase__      =	collections.defaultdict(_lowerCamelCase	)
											for i, p in enumerate(probabilities.squeeze().tolist()	):
													lowercase__      =	token_type_ids_example[:, 0].tolist()[i]
													lowercase__      =	token_type_ids_example[:, 1].tolist()[i] - 1
													lowercase__      =	token_type_ids_example[:, 2].tolist()[i] - 1
													if col >= 0 and row >= 0 and segment_id == 1:
															coords_to_probs[(col, row)].append(_lowerCamelCase	)

											lowercase__      =	{key: np.array(coords_to_probs[key]	).mean() > 0.5 for key in coords_to_probs}

									lowercase__      =	torch.cat(tuple(_lowerCamelCase	)   ,      0	)

									return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_lowerCamelCase	)   ,      0	))
							else:
									lowercase__      =	[]
									lowercase__      =	[]
									lowercase__      =	None
									lowercase__      =	inputs["""input_ids"""].shape[0]

									lowercase__      =	inputs["""input_ids"""]
									lowercase__      =	inputs["""attention_mask"""]
									lowercase__      =	inputs["""token_type_ids"""].numpy()
									lowercase__      =	None

									for index in range(_lowerCamelCase	):
											# If sequences have already been processed, the token type IDs will be created according to the previous
											# answer.
											if prev_answers is not None:
													lowercase__      =	token_type_ids_example[:, 3]  # shape (seq_len,)
													lowercase__      =	np.zeros_like(_lowerCamelCase   ,      dtype=np.intaa	)  # shape (seq_len,)

													lowercase__      =	token_type_ids[index]  # shape (seq_len, 7)
													for i in range(model_labels.shape[0]	):
															lowercase__      =	token_type_ids_example[:, 0].tolist()[i]
															lowercase__      =	token_type_ids_example[:, 1].tolist()[i] - 1
															lowercase__      =	token_type_ids_example[:, 2].tolist()[i] - 1

															if row_id >= 0 and col_id >= 0 and segment_id == 1:
																	lowercase__      =	int(prev_answers[(col_id, row_id)]	)

													lowercase__      =	model_labels

											lowercase__      =	input_ids[index]
											lowercase__      =	attention_mask[index]  # shape (seq_len,)
											lowercase__      =	token_type_ids[index]  # shape (seq_len, 7)
											lowercase__      =	self.model(
											    input_ids=np.expand_dims(_lowerCamelCase   ,      axis=0	)   ,      attention_mask=np.expand_dims(_lowerCamelCase   ,      axis=0	)   ,      token_type_ids=np.expand_dims(_lowerCamelCase   ,      axis=0	)   ,      )
											lowercase__      =	outputs.logits

											if self.aggregate:
													all_aggregations.append(outputs.logits_aggregation	)

											all_logits.append(_lowerCamelCase	)

											lowercase__      =	tfp.distributions.Bernoulli(logits=_lowerCamelCase	)
											lowercase__      =	dist_per_token.probs_parameter() * tf.cast(_lowerCamelCase   ,      tf.floataa	)

											lowercase__      =	collections.defaultdict(_lowerCamelCase	)
											lowercase__      =	token_type_ids_example
											for i, p in enumerate(tf.squeeze(_lowerCamelCase	).numpy().tolist()	):
													lowercase__      =	token_type_ids_example[:, 0].tolist()[i]
													lowercase__      =	token_type_ids_example[:, 1].tolist()[i] - 1
													lowercase__      =	token_type_ids_example[:, 2].tolist()[i] - 1
													if col >= 0 and row >= 0 and segment_id == 1:
															coords_to_probs[(col, row)].append(_lowerCamelCase	)

											lowercase__      =	{key: np.array(coords_to_probs[key]	).mean() > 0.5 for key in coords_to_probs}

									lowercase__      =	tf.concat(tuple(_lowerCamelCase	)   ,      0	)

									return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(_lowerCamelCase	)   ,      0	))

					def __call__(self					:		List[Any]   ,      *_UpperCAmelCase					:		Optional[Any]   ,      **_UpperCAmelCase					:		int	)		->     Any:




							"""simple docstring"""


							lowercase__      =	self._args_parser(*_lowerCamelCase   ,      **_lowerCamelCase	)

							lowercase__      =	super().__call__(_lowerCamelCase   ,      **_lowerCamelCase	)
							if len(_lowerCamelCase	) == 1:
									return results[0]
							return results

					def 						lowerCamelCase__  (self					:		Tuple   ,      _UpperCAmelCase					:		Optional[int]=None   ,      _UpperCAmelCase					:		str=None   ,      _UpperCAmelCase					:		Optional[Any]=None   ,      **_UpperCAmelCase					:		List[Any]	)		->     List[str]:




							"""simple docstring"""


							lowercase__      =	{}
							if padding is not None:
									lowercase__      =	padding
							if truncation is not None:
									lowercase__      =	truncation

							lowercase__      =	{}
							if sequential is not None:
									lowercase__      =	sequential
							return preprocess_params, forward_params, {}

					def 						lowerCamelCase__  (self					:		Dict   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		Tuple=None   ,      _UpperCAmelCase					:		Any=True   ,      _UpperCAmelCase					:		Union[str, Any]=None	)		->     Tuple:




							"""simple docstring"""


							if truncation is None:
									if self.type == "tapas":
											lowercase__      =	"""drop_rows_to_fit"""
									else:
											lowercase__      =	"""do_not_truncate"""

							lowercase__      =	pipeline_input["""table"""], pipeline_input["""query"""]
							if table.empty:
									raise ValueError("""table is empty"""	)
							if query is None or query == "":
									raise ValueError("""query is empty"""	)
							lowercase__      =	self.tokenizer(_lowerCamelCase   ,      _lowerCamelCase   ,      return_tensors=self.framework   ,      truncation=_lowerCamelCase   ,      padding=_lowerCamelCase	)
							lowercase__      =	table
							return inputs

					def 						lowerCamelCase__  (self					:		Optional[int]   ,      _UpperCAmelCase					:		Any   ,      _UpperCAmelCase					:		Union[str, Any]=False	)		->     Optional[int]:




							"""simple docstring"""


							lowercase__      =	model_inputs.pop("""table"""	)

							if self.type == "tapas":
									if sequential:
											lowercase__      =	self.sequential_inference(**_lowerCamelCase	)
									else:
											lowercase__      =	self.batch_inference(**_lowerCamelCase	)
							else:
									lowercase__      =	self.model.generate(**_lowerCamelCase	)
							lowercase__      =	{"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
							return model_outputs


					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		int	)		->     int:




							"""simple docstring"""


							lowercase__      =	model_outputs["""model_inputs"""]
							lowercase__      =	model_outputs["""table"""]
							lowercase__      =	model_outputs["""outputs"""]
							if self.type == "tapas":
									if self.aggregate:
											lowercase__      =	outputs[:2]
											lowercase__      =	self.tokenizer.convert_logits_to_predictions(_lowerCamelCase   ,      _lowerCamelCase   ,      _lowerCamelCase	)
											lowercase__      =	predictions
											lowercase__      =	{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_lowerCamelCase	)}

											lowercase__      =	self.model.config.no_aggregation_label_index
											lowercase__      =	{
											    i: aggregators[i] + """ > """ for i, pred in enumerate(_lowerCamelCase	) if pred != no_agg_label_index
											}
									else:
											lowercase__      =	outputs[0]
											lowercase__      =	self.tokenizer.convert_logits_to_predictions(_lowerCamelCase   ,      _lowerCamelCase	)
											lowercase__      =	predictions[0]
											lowercase__      =	{}
											lowercase__      =	{}
									lowercase__      =	[]
									for index, coordinates in enumerate(_lowerCamelCase	):
											lowercase__      =	[table.iat[coordinate] for coordinate in coordinates]
											lowercase__      =	aggregators.get(_lowerCamelCase   ,      """"""	)
											lowercase__      =	aggregators_prefix.get(_lowerCamelCase   ,      """"""	)
											lowercase__      =	{
											    """answer""": aggregator_prefix + """, """.join(_lowerCamelCase	),
											    """coordinates""": coordinates,
											    """cells""": [table.iat[coordinate] for coordinate in coordinates],
											}
											if aggregator:
													lowercase__      =	aggregator

											answers.append(_lowerCamelCase	)
									if len(_lowerCamelCase	) == 0:
											raise PipelineException("""Empty answer"""	)
							else:
									lowercase__      =	[{"""answer""": answer} for answer in self.tokenizer.batch_decode(_lowerCamelCase   ,      skip_special_tokens=_lowerCamelCase	)]

							return answers if len(_lowerCamelCase	) > 1 else answers[0]





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					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		int=False   ,      _UpperCAmelCase					:		List[str]=1024   ,      _UpperCAmelCase					:		Dict=1024   ,      _UpperCAmelCase					:		List[str]=3.6   ,      _UpperCAmelCase					:		Optional[int]=False   ,      )		->     List[str]:




							"""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					:		Dict	)		->     Any:




							"""simple docstring"""


							lowercase__      =	iter(self.dataset	)
							lowercase__      =	True
							while more_examples:
									lowercase__    ,			lowercase__      =	[], 0
									while True:
											if buffer_len >= self.max_buffer_size:
													break
											try:
													buffer.append(next(_UpperCAmelCase	)[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(_UpperCAmelCase   ,      truncation=_UpperCAmelCase	)["""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(_UpperCAmelCase	)   ,      self.seq_length	):
											lowercase__      =	all_token_ids[i : i + self.seq_length]
											if len(_UpperCAmelCase	) == self.seq_length:
													self.current_size += 1
													yield torch.tensor(_UpperCAmelCase	)


					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		Optional[Any]=1000	)		->     Optional[int]:




							"""simple docstring"""


							return ShufflerIterDataPipe(self   ,      buffer_size=_UpperCAmelCase	)






def 				UpperCamelCase      (			__magic_name__  :							Optional[int]  )     ->						Tuple:
		"""simple docstring"""




		lowercase__      =	args.model_ckpt.split("""/"""  )[-1]
		lowercase__      =	logging.getLogger(__name__  )
		lowercase__      =	Path(args.save_dir  ) / """log/"""
		log_dir.mkdir(exist_ok=__magic_name__  )
		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(__magic_name__					,					vars(__magic_name__  )  )
				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      (			__magic_name__  :							Optional[Any]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__      =	{"""streaming""": True}
		lowercase__      =	load_dataset(args.dataset_name_train					,					split="""train"""					,					**__magic_name__  )
		lowercase__      =	train_data.shuffle(buffer_size=args.shuffle_buffer					,					seed=args.seed  )
		lowercase__      =	load_dataset(args.dataset_name_valid					,					split="""train"""					,					**__magic_name__  )
		lowercase__      =	ConstantLengthDataset(
		    __magic_name__					,					__magic_name__					,					infinite=__magic_name__					,					seq_length=args.seq_length					,					tokenized=args.tokenized  )
		lowercase__      =	ConstantLengthDataset(
		    __magic_name__					,					__magic_name__					,					infinite=__magic_name__					,					seq_length=args.seq_length					,					tokenized=args.tokenized  )
		lowercase__      =	train_dataset.shuffle(buffer_size=args.shuffle_buffer  )
		lowercase__      =	DataLoader(__magic_name__					,					batch_size=args.train_batch_size					,					shuffle=__magic_name__  )
		lowercase__      =	DataLoader(__magic_name__					,					batch_size=args.valid_batch_size  )
		return train_dataloader, eval_dataloader






def 				UpperCamelCase      (			__magic_name__  :							int					,					__magic_name__  :							List[Any]					,					__magic_name__  :							int=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]  )     ->						List[str]:
		"""simple docstring"""




		lowercase__    ,			lowercase__      =	[], []
		for n, p in model.named_parameters():
				if any(nd in n for nd in no_decay  ):
						params_without_wd.append(__magic_name__  )
				else:
						params_with_wd.append(__magic_name__  )
		return [
		    {"params": params_with_wd, "weight_decay": args.weight_decay},
		    {"params": params_without_wd, "weight_decay": 0.0},
		]






def 				UpperCamelCase      (			__magic_name__  :							Tuple					,					__magic_name__  :							Dict  )     ->						Optional[int]:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str					,					__magic_name__  :							Union[str, Any]					,					__magic_name__  :							List[str]  )     ->						str:
		"""simple docstring"""




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






def 				UpperCamelCase      (			__magic_name__  :							str  )     ->						int:
		"""simple docstring"""




		model.eval()
		lowercase__      =	[]
		for step, batch in enumerate(__magic_name__  ):
				with torch.no_grad():
						lowercase__      =	model(__magic_name__					,					labels=__magic_name__  )
				lowercase__      =	outputs.loss.repeat(args.valid_batch_size  )
				losses.append(accelerator.gather(__magic_name__  )  )
				if args.max_eval_steps > 0 and step >= args.max_eval_steps:
						break
		lowercase__      =	torch.cat(__magic_name__  )
		lowercase__      =	losses[: eval_dataloader.dataset.current_size].mean()
		try:
				lowercase__      =	torch.exp(__magic_name__  )
		except OverflowError:
				lowercase__      =	float("""inf"""  )
		return loss.item(), perplexity.item()


# Settings
A :	Any												=     HfArgumentParser(TrainingArguments)
A :	int												=     parser.parse_args()

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

A :	Optional[int]												=     Namespace(**vars(args), **acc_state)
A :	Any												=     accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

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

# Logging
A						,   A :	Union[str, Any]												=     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
A :	Tuple												=     AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
	model.gradient_checkpointing_enable()
A :	List[Any]												=     AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
A						,   A :	List[Any]												=     create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
A :	int												=     AdamW(get_grouped_params(model, args), lr=args.learning_rate)
A :	int												=     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      (			)     ->						Any:
		"""simple docstring"""




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


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

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

