
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     :    Dict      							=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :    Tuple      							=					{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Dict	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		List[Any]							)    ->     Any:
				for attribute in key.split('''.'''							):
								__lowerCamelCase					      =			getattr(__lowerCAmelCase	,						__lowerCAmelCase							)

				if weight_type is not None:
								__lowerCamelCase					      =			getattr(__lowerCAmelCase	,						__lowerCAmelCase							).shape
				else:
								__lowerCamelCase					      =			hf_pointer.shape

				assert hf_shape == value.shape, (
				    f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
				    f''' {value.shape} for {full_name}'''
				)

				if weight_type == "weight":
								__lowerCamelCase					      =			value
				elif weight_type == "weight_g":
								__lowerCamelCase					      =			value
				elif weight_type == "weight_v":
								__lowerCamelCase					      =			value
				elif weight_type == "bias":
								__lowerCamelCase					      =			value
				else:
								__lowerCamelCase					      =			value

				logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''							)




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		List[Any]							)    ->     List[Any]:
				__lowerCamelCase					      =			[]
				__lowerCamelCase					      =			fairseq_model.state_dict()

				__lowerCamelCase					      =			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__lowerCamelCase					      =			False
								if "conv_layers" in name:
												load_conv_layer(
												    __lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase	,						hf_model.config.feat_extract_norm == '''group'''	,						)
												__lowerCamelCase					      =			True
								else:
												for key, mapped_key in MAPPING.items():
																__lowerCamelCase					      =			'''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

																if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
																				__lowerCamelCase					      =			True
																				if "*" in mapped_key:
																								__lowerCamelCase					      =			name.split(__lowerCAmelCase							)[0].split('''.'''							)[-2]
																								if not layer_index.isnumeric():
																												continue
																								__lowerCamelCase					      =			mapped_key.replace('''*'''	,						__lowerCAmelCase							)
																				if "weight_g" in name:
																								__lowerCamelCase					      =			'''weight_g'''
																				elif "weight_v" in name:
																								__lowerCamelCase					      =			'''weight_v'''
																				elif "weight" in name:
																								__lowerCamelCase					      =			'''weight'''
																				elif "bias" in name:
																								__lowerCamelCase					      =			'''bias'''
																				else:
																								__lowerCamelCase					      =			None
																				set_recursively(__lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase							)
																continue
								if not is_used:
												unused_weights.append(__lowerCAmelCase							)

				logger.warning(f'''Unused weights: {unused_weights}'''							)




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		Union[str, Any]	,						__lowerCAmelCase			:		str	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Optional[Any]							)    ->     Any:
				__lowerCamelCase					      =			full_name.split('''conv_layers.'''							)[-1]
				__lowerCamelCase					      =			name.split('''.'''							)
				__lowerCamelCase					      =			int(items[0]							)
				__lowerCamelCase					      =			int(items[1]							)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
												)
												__lowerCamelCase					      =			value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
												)
												__lowerCamelCase					      =			value
												logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
												    " found."
												)
												__lowerCamelCase					      =			value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f'''{full_name} has size {value.shape}, but'''
												    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
												)
												__lowerCamelCase					      =			value
												logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
				else:
								unused_weights.append(__lowerCAmelCase							)




def 				__magic_name__				(     __lowerCAmelCase			:		Tuple	,						__lowerCAmelCase			:		List[str]							)    ->     int:
				__lowerCamelCase					      =			SEWDConfig()
				if is_finetuned:
								__lowerCamelCase					      =			model.wav_encoder.wav_model.cfg
				else:
								__lowerCamelCase					      =			model.cfg

				__lowerCamelCase					      =			fs_config.conv_bias
				__lowerCamelCase					      =			eval(fs_config.conv_feature_layers							)
				__lowerCamelCase					      =			[x[0] for x in conv_layers]
				__lowerCamelCase					      =			[x[1] for x in conv_layers]
				__lowerCamelCase					      =			[x[2] for x in conv_layers]
				__lowerCamelCase					      =			'''gelu'''
				__lowerCamelCase					      =			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
				__lowerCamelCase					      =			0.0
				__lowerCamelCase					      =			fs_config.activation_fn.name
				__lowerCamelCase					      =			fs_config.encoder_embed_dim
				__lowerCamelCase					      =			0.02
				__lowerCamelCase					      =			fs_config.encoder_ffn_embed_dim
				__lowerCamelCase					      =			1E-5
				__lowerCamelCase					      =			fs_config.encoder_layerdrop
				__lowerCamelCase					      =			fs_config.encoder_attention_heads
				__lowerCamelCase					      =			fs_config.conv_pos_groups
				__lowerCamelCase					      =			fs_config.conv_pos
				__lowerCamelCase					      =			len(__lowerCAmelCase							)
				__lowerCamelCase					      =			fs_config.encoder_layers
				__lowerCamelCase					      =			fs_config.squeeze_factor
				# DeBERTa-specific parameters:
				__lowerCamelCase					      =			fs_config.max_position_embeddings
				__lowerCamelCase					      =			fs_config.position_buckets
				__lowerCamelCase					      =			fs_config.share_att_key
				__lowerCamelCase					      =			fs_config.relative_attention
				__lowerCamelCase					      =			fs_config.position_biased_input
				__lowerCamelCase					      =			tuple(fs_config.pos_att_type.split('''|'''							)							)
				__lowerCamelCase					      =			fs_config.norm_rel_ebd

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								__lowerCamelCase					      =			model.cfg
								__lowerCamelCase					      =			fs_config.final_dropout
								__lowerCamelCase					      =			fs_config.layerdrop
				__lowerCamelCase					      =			fs_config.activation_dropout
				__lowerCamelCase					      =			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				__lowerCamelCase					      =			fs_config.attention_dropout
				__lowerCamelCase					      =			fs_config.dropout_input
				__lowerCamelCase					      =			fs_config.dropout
				__lowerCamelCase					      =			fs_config.mask_channel_length
				__lowerCamelCase					      =			fs_config.mask_channel_prob
				__lowerCamelCase					      =			fs_config.mask_length
				__lowerCamelCase					      =			fs_config.mask_prob

				__lowerCamelCase					      =			'''Wav2Vec2FeatureExtractor'''
				__lowerCamelCase					      =			'''Wav2Vec2CTCTokenizer'''

				return config




@torch.no_grad()
def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		List[Any]	,						__lowerCAmelCase			:		Tuple=None	,						__lowerCAmelCase			:		List[str]=None	,						__lowerCAmelCase			:		Union[str, Any]=True							)    ->     Dict:

				if is_finetuned:
								__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]	,						arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
				else:
								__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

				if config_path is not None:
								__lowerCamelCase					      =			SEWDConfig.from_pretrained(__lowerCAmelCase							)
				else:
								__lowerCamelCase					      =			convert_config(model[0]	,						__lowerCAmelCase							)
				__lowerCamelCase					      =			model[0].eval()

				__lowerCamelCase					      =			True if config.feat_extract_norm == '''layer''' else False
				__lowerCamelCase					      =			WavaVecaFeatureExtractor(
				    feature_size=1	,						sampling_rate=1_6000	,						padding_value=0	,						do_normalize=__lowerCAmelCase	,						return_attention_mask=__lowerCAmelCase	,						)

				if is_finetuned:
								if dict_path:
												__lowerCamelCase					      =			Dictionary.load(__lowerCAmelCase							)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												__lowerCamelCase					      =			target_dict.pad_index
												__lowerCamelCase					      =			target_dict.bos_index
												__lowerCamelCase					      =			target_dict.pad_index
												__lowerCamelCase					      =			target_dict.bos_index
												__lowerCamelCase					      =			target_dict.eos_index
												__lowerCamelCase					      =			len(target_dict.symbols							)
												__lowerCamelCase					      =			os.path.join(__lowerCAmelCase	,						'''vocab.json'''							)
												if not os.path.isdir(__lowerCAmelCase							):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCAmelCase							)							)
																return
												os.makedirs(__lowerCAmelCase	,						exist_ok=__lowerCAmelCase							)
												with open(__lowerCAmelCase	,						'''w'''	,						encoding='''utf-8'''							) as vocab_handle:
																json.dump(target_dict.indices	,						__lowerCAmelCase							)
												__lowerCamelCase					      =			WavaVecaCTCTokenizer(
												    __lowerCAmelCase	,						unk_token=target_dict.unk_word	,						pad_token=target_dict.pad_word	,						bos_token=target_dict.bos_word	,						eos_token=target_dict.eos_word	,						word_delimiter_token='''|'''	,						do_lower_case=__lowerCAmelCase	,						)
												__lowerCamelCase					      =			WavaVecaProcessor(feature_extractor=__lowerCAmelCase	,						tokenizer=__lowerCAmelCase							)
												processor.save_pretrained(__lowerCAmelCase							)

								__lowerCamelCase					      =			SEWDForCTC(__lowerCAmelCase							)
				else:
								__lowerCamelCase					      =			SEWDModel(__lowerCAmelCase							)
								feature_extractor.save_pretrained(__lowerCAmelCase							)

				recursively_load_weights(__lowerCAmelCase	,						__lowerCAmelCase	,						__lowerCAmelCase							)

				hf_model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__     :    List[Any]      							=					argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)





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__  (			__lowercase    ):






		def __init__(	self	:							str							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Dict							,    SCREAMING_SNAKE_CASE__	:							Any=False							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=10_24							,    SCREAMING_SNAKE_CASE__	:							Dict=10_24							,    SCREAMING_SNAKE_CASE__	:							str=3.6							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=False							,    )  ->							Optional[int]:
						__lowerCamelCase					      =			tokenizer
						__lowerCamelCase					      =			tokenizer.bos_token_id
						__lowerCamelCase					      =			dataset
						__lowerCamelCase					      =			seq_length
						__lowerCamelCase					      =			0
						__lowerCamelCase					      =			infinite
						__lowerCamelCase					      =			0
						__lowerCamelCase					      =			tokenized

						if self.tokenized:
										__lowerCamelCase					      =			seq_length * num_of_sequences
										__lowerCamelCase					      =			'''input_ids'''
						else:
										__lowerCamelCase					      =			seq_length * chars_per_token * num_of_sequences
										__lowerCamelCase					      =			'''content'''






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







		def 				__A (	self	:							List[str]							,    SCREAMING_SNAKE_CASE__	:							str=10_00						)  ->							Union[str, Any]:
						return ShufflerIterDataPipe(self							,    buffer_size=SCREAMING_SNAKE_CASE__						)





def 				__magic_name__				(     __lowerCAmelCase			:		Tuple							)    ->     Dict:
				__lowerCamelCase					      =			args.model_ckpt.split('''/'''							)[-1]
				__lowerCamelCase					      =			logging.getLogger(__name__							)
				__lowerCamelCase					      =			Path(args.save_dir							) / '''log/'''
				log_dir.mkdir(exist_ok=__lowerCAmelCase							)
				__lowerCamelCase					      =			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					      =			accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO							)
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								__lowerCamelCase					      =			''''''
								logger.setLevel(logging.ERROR							)
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name




def 				__magic_name__				(     __lowerCAmelCase			:		Optional[Any]							)    ->     Tuple:
				__lowerCamelCase					      =			{'''streaming''': True}
				__lowerCamelCase					      =			load_dataset(args.dataset_name_train	,						split='''train'''	,						**__lowerCAmelCase							)
				__lowerCamelCase					      =			train_data.shuffle(buffer_size=args.shuffle_buffer	,						seed=args.seed							)
				__lowerCamelCase					      =			load_dataset(args.dataset_name_valid	,						split='''train'''	,						**__lowerCAmelCase							)
				__lowerCamelCase					      =			ConstantLengthDataset(
				    __lowerCAmelCase	,						__lowerCAmelCase	,						infinite=__lowerCAmelCase	,						seq_length=args.seq_length	,						tokenized=args.tokenized							)
				__lowerCamelCase					      =			ConstantLengthDataset(
				    __lowerCAmelCase	,						__lowerCAmelCase	,						infinite=__lowerCAmelCase	,						seq_length=args.seq_length	,						tokenized=args.tokenized							)
				__lowerCamelCase					      =			train_dataset.shuffle(buffer_size=args.shuffle_buffer							)
				__lowerCamelCase					      =			DataLoader(__lowerCAmelCase	,						batch_size=args.train_batch_size	,						shuffle=__lowerCAmelCase							)
				__lowerCamelCase					      =			DataLoader(__lowerCAmelCase	,						batch_size=args.valid_batch_size							)
				return train_dataloader, eval_dataloader




def 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Optional[Any]	,						__lowerCAmelCase			:		Union[str, Any]=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]							)    ->     List[str]:
				__lowerCamelCase      ,					__lowerCamelCase					      =			[], []
				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 				__magic_name__				(     __lowerCAmelCase			:		int	,						__lowerCAmelCase			:		Union[str, Any]							)    ->     str:
				logger.info(f'''Step {step}: {metrics}'''							)
				if accelerator.is_main_process:
								accelerator.log(__lowerCAmelCase	,						__lowerCAmelCase							)




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




def 				__magic_name__				(     __lowerCAmelCase			:		Dict							)    ->     Optional[Any]:
				model.eval()
				__lowerCamelCase					      =			[]
				for step, batch in enumerate(__lowerCAmelCase							):
								with torch.no_grad():
												__lowerCamelCase					      =			model(__lowerCAmelCase	,						labels=__lowerCAmelCase							)
								__lowerCamelCase					      =			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					      =			torch.cat(__lowerCAmelCase							)
				__lowerCamelCase					      =			losses[: eval_dataloader.dataset.current_size].mean()
				try:
								__lowerCamelCase					      =			torch.exp(__lowerCAmelCase							)
				except OverflowError:
								__lowerCamelCase					      =			float('''inf'''							)
				return loss.item(), perplexity.item()


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

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

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

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

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

# Load dataset and dataloader
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[str]      							=					create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					AdamW(get_grouped_params(model, args), lr=args.learning_rate)
SCREAMING_SNAKE_CASE__     :    Tuple      							=					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 				__magic_name__				(     )    ->     Any:
				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Tuple      							=					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__     :    Tuple      							=					os.path.basename(args.resume_from_checkpoint)
					else:
										# Get the most recent checkpoint
										SCREAMING_SNAKE_CASE__     :    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)
										SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					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__     :    str      							=					os.path.splitext(path)[0]
					SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					int(training_difference.replace("step_", ""))

# Train model
model.train()
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					0
SCREAMING_SNAKE_CASE__     :    Any      							=					time.time()
SCREAMING_SNAKE_CASE__     :    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
					SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					model(batch, labels=batch, use_cache=False).loss
					SCREAMING_SNAKE_CASE__     :    str      							=					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__     :    Optional[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:
										SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					get_lr()
										accelerator.backward(loss)
										accelerator.clip_grad_norm_(model.parameters(), 1.0)
										optimizer.step()
										lr_scheduler.step()
										optimizer.zero_grad()
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					time.time() - t_start
										SCREAMING_SNAKE_CASE__     :    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,
										    },
										)
										SCREAMING_SNAKE_CASE__     :    Tuple      							=					time.time()
										SCREAMING_SNAKE_CASE__     :    str      							=					0
										completed_steps += 1
					if step % args.save_checkpoint_steps == 0:
										logger.info("Evaluating and saving model checkpoint")
										SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					evaluate(args)
										log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
										accelerator.wait_for_everyone()
										SCREAMING_SNAKE_CASE__     :    Optional[int]      							=					os.path.join(args.save_dir, F'step_{step}')
										accelerator.save_state(save_dir)
										if accelerator.is_main_process:
															hf_repo.push_to_hub(commit_message=F'step {step}')
										model.train()
					if completed_steps >= args.max_train_steps:
										break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__     :    List[Any]      							=					evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE__     :    Optional[Any]      							=					accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
SCREAMING_SNAKE_CASE__     :    List[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")



