





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

__UpperCAmelCase							:   Dict							=  {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__UpperCAmelCase							:   Optional[Any]							=  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 					lowercase_ (    __snake_case     :							Optional[int]      )     -> List[str]:





				'''simple docstring'''


				snake_case__      :Optional[int]         =   {}
				with open(__snake_case	,	"r"      ) as file:
								for line_number, line in enumerate(__snake_case      ):
												snake_case__      :Any         =   line.strip()
												if line:
																snake_case__      :Optional[int]         =   line.split()
																snake_case__      :int         =   line_number
																snake_case__      :Tuple         =   words[0]
																snake_case__      :str         =   value
				return result





def 					lowercase_ (    __snake_case     :							Dict	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							int	,	__snake_case     :							Optional[int]	,	__snake_case     :							List[Any]      )     -> int:





				'''simple docstring'''


				for attribute in key.split("."      ):
								snake_case__      :Union[str, Any]         =   getattr(__snake_case	,	__snake_case      )

				snake_case__      :int         =   None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(__snake_case      ):
												snake_case__      :Union[str, Any]         =   PARAM_MAPPING[full_name.split("."      )[-1]]
												snake_case__      :List[str]         =   "param"

				if weight_type is not None and weight_type != "param":
								snake_case__      :Union[str, Any]         =   getattr(__snake_case	,	__snake_case      ).shape
				elif weight_type is not None and weight_type == "param":
								snake_case__      :Optional[Any]         =   hf_pointer
								for attribute in hf_param_name.split("."      ):
												snake_case__      :Any         =   getattr(__snake_case	,	__snake_case      )
								snake_case__      :Tuple         =   shape_pointer.shape

								# let's reduce dimension
								snake_case__      :Tuple         =   value[0]
				else:
								snake_case__      :Union[str, Any]         =   hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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":
								snake_case__      :Tuple         =   value
				elif weight_type == "weight_g":
								snake_case__      :Any         =   value
				elif weight_type == "weight_v":
								snake_case__      :Tuple         =   value
				elif weight_type == "bias":
								snake_case__      :Optional[int]         =   value
				elif weight_type == "param":
								for attribute in hf_param_name.split("."      ):
												snake_case__      :Dict         =   getattr(__snake_case	,	__snake_case      )
								snake_case__      :Tuple         =   value
				else:
								snake_case__      :List[str]         =   value

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





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Optional[int]	,	__snake_case     :							Optional[Any]	,	__snake_case     :							List[Any]	,	__snake_case     :							Optional[int]      )     -> str:





				'''simple docstring'''


				snake_case__      :str         =   None
				for param_key in PARAM_MAPPING.keys():
								if full_name.endswith(__snake_case      ):
												snake_case__      :Optional[Any]         =   PARAM_MAPPING[full_name.split("."      )[-1]]
												snake_case__      :Union[str, Any]         =   "param"

				if weight_type is not None and weight_type != "param":
								snake_case__      :List[str]         =   ".".join([key, weight_type]      )
				elif weight_type is not None and weight_type == "param":
								snake_case__      :Optional[int]         =   ".".join([key, hf_param_name]      )
				else:
								snake_case__      :List[str]         =   key

				snake_case__      :Tuple         =   value if "lm_head" in full_key else value[0]


__UpperCAmelCase							:   List[Any]							=  {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							str	,	__snake_case     :							Optional[Any]=None	,	__snake_case     :							Optional[int]=None      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :Tuple         =   False
				for key, mapped_key in MAPPING.items():
								snake_case__      :Optional[int]         =   "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split("w2v_model."      )[-1] == name.split("."      )[0]:
												snake_case__      :Optional[int]         =   True
												if "*" in mapped_key:
																snake_case__      :Dict         =   name.split(__snake_case      )[0].split("."      )[-2]
																snake_case__      :List[str]         =   mapped_key.replace("*"	,	__snake_case      )
												if "weight_g" in name:
																snake_case__      :List[Any]         =   "weight_g"
												elif "weight_v" in name:
																snake_case__      :Optional[Any]         =   "weight_v"
												elif "bias" in name:
																snake_case__      :Optional[int]         =   "bias"
												elif "weight" in name:
																# TODO: don't match quantizer.weight_proj
																snake_case__      :Optional[int]         =   "weight"
												else:
																snake_case__      :int         =   None
												if hf_dict is not None:
																rename_dict(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
												else:
																set_recursively(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
												return is_used
				return is_used





def 					lowercase_ (    __snake_case     :							str	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							Any      )     -> int:





				'''simple docstring'''


				snake_case__      :Any         =   []
				snake_case__      :Optional[Any]         =   fairseq_model.state_dict()

				snake_case__      :List[Any]         =   hf_model.wavaveca.feature_extractor

				for name, value in fairseq_dict.items():
								snake_case__      :Any         =   False
								if "conv_layers" in name:
												load_conv_layer(
												    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	hf_model.config.feat_extract_norm == "group"	,	)
												snake_case__      :int         =   True
								else:
												snake_case__      :str         =   load_wavaveca_layer(__snake_case	,	__snake_case	,	__snake_case      )
								if not is_used:
												unused_weights.append(__snake_case      )

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





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							List[str]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							int	,	__snake_case     :							Dict      )     -> str:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   full_name.split("conv_layers."      )[-1]
				snake_case__      :Union[str, Any]         =   name.split("."      )
				snake_case__      :List[str]         =   int(items[0]      )
				snake_case__      :Optional[Any]         =   int(items[1]      )

				if type_id == 0:
								if "bias" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    F'{full_name} has size {value.shape}, but'
																    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'      )
												snake_case__      :Optional[Any]         =   value
												logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'      )
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    F'{full_name} has size {value.shape}, but'
																    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'      )
												snake_case__      :Dict         =   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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F'{full_name} has size {value.shape}, but'
																    F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'      )
												snake_case__      :Tuple         =   value
												logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
								elif "weight" in name:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F'{full_name} has size {value.shape}, but'
																    F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'      )
												snake_case__      :Optional[int]         =   value
												logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
				else:
								unused_weights.append(__snake_case      )





@torch.no_grad()
def 					lowercase_ (    __snake_case     :							Dict	,	__snake_case     :							int	,	__snake_case     :							str=None	,	__snake_case     :							List[Any]=None	,	__snake_case     :							Dict=True	,	__snake_case     :							Tuple=False      )     -> Optional[Any]:





				'''simple docstring'''


				if config_path is not None:
								snake_case__      :List[str]         =   WavaVecaConfig.from_pretrained(__snake_case      )
				else:
								snake_case__      :int         =   WavaVecaConfig()

				if is_seq_class:
								snake_case__      :str         =   read_txt_into_dict(__snake_case      )
								snake_case__      :Optional[Any]         =   idalabel
								snake_case__      :Optional[int]         =   WavaVecaForSequenceClassification(__snake_case      )
								snake_case__      :List[str]         =   WavaVecaFeatureExtractor(
								    feature_size=1	,	sampling_rate=1_60_00	,	padding_value=0	,	do_normalize=__snake_case	,	return_attention_mask=__snake_case	,	)
								feature_extractor.save_pretrained(__snake_case      )

				elif is_finetuned:
								if dict_path:
												snake_case__      :Tuple         =   Dictionary.load(__snake_case      )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												snake_case__      :str         =   target_dict.pad_index
												snake_case__      :Tuple         =   target_dict.bos_index
												snake_case__      :Union[str, Any]         =   target_dict.eos_index
												snake_case__      :Union[str, Any]         =   len(target_dict.symbols      )
												snake_case__      :List[str]         =   os.path.join(__snake_case	,	"vocab.json"      )
												if not os.path.isdir(__snake_case      ):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__snake_case      )      )
																return
												os.makedirs(__snake_case	,	exist_ok=__snake_case      )
												snake_case__      :Any         =   target_dict.indices

												# fairseq has the <pad> and <s> switched
												snake_case__      :Optional[Any]         =   0
												snake_case__      :str         =   1
												with open(__snake_case	,	"w"	,	encoding="utf-8"      ) as vocab_handle:
																json.dump(__snake_case	,	__snake_case      )
												snake_case__      :Union[str, Any]         =   WavaVecaCTCTokenizer(
												    __snake_case	,	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=__snake_case	,	)
												snake_case__      :int         =   True if config.feat_extract_norm == "layer" else False
												snake_case__      :Optional[int]         =   WavaVecaFeatureExtractor(
												    feature_size=1	,	sampling_rate=1_60_00	,	padding_value=0	,	do_normalize=__snake_case	,	return_attention_mask=__snake_case	,	)
												snake_case__      :List[Any]         =   WavaVecaProcessor(feature_extractor=__snake_case	,	tokenizer=__snake_case      )
												processor.save_pretrained(__snake_case      )

								snake_case__      :Tuple         =   WavaVecaForCTC(__snake_case      )
				else:
								snake_case__      :List[str]         =   WavaVecaForPreTraining(__snake_case      )

				if is_finetuned or is_seq_class:
								snake_case__      :Optional[Any]         =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path]	,	arg_overrides={"data": "/".join(dict_path.split("/"      )[:-1]      )}      )
				else:
								snake_case__      :Optional[Any]         =   argparse.Namespace(task="audio_pretraining"      )
								snake_case__      :Optional[Any]         =   fairseq.tasks.setup_task(__snake_case      )

								snake_case__      :List[Any]         =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,	task=__snake_case      )

				snake_case__      :Any         =   model[0].eval()

				recursively_load_weights(__snake_case	,	__snake_case	,	not is_finetuned      )

				hf_wavavec.save_pretrained(__snake_case      )


if __name__ == "__main__":
	__UpperCAmelCase							:   int							=  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(
	    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
	)
	parser.add_argument(
	    "--is_seq_class",
	    action="store_true",
	    help="Whether the model to convert is a fine-tuned sequence classification model or not",
	)
	__UpperCAmelCase							:   Optional[int]							=  parser.parse_args()

	__UpperCAmelCase							:   Optional[Any]							=  not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)







import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__UpperCAmelCase							:   Optional[Any]							=  1_6
__UpperCAmelCase							:   Optional[int]							=  3_2





def 					lowercase_ (    __snake_case     :							Accelerator	,	__snake_case     :							int = 16	,	__snake_case     :							str = "bert-base-cased"      )     -> Optional[Any]:





				'''simple docstring'''


				snake_case__      :int         =   AutoTokenizer.from_pretrained(__snake_case      )
				snake_case__      :Optional[int]         =   load_dataset("glue"	,	"mrpc"      )

				def tokenize_function(__snake_case     :							Tuple      ):
								# max_length=None => use the model max length (it's actually the default)
								snake_case__      :Any         =   tokenizer(examples["sentence1"]	,	examples["sentence2"]	,	truncation=__snake_case	,	max_length=__snake_case      )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				snake_case__      :List[Any]         =   datasets.map(
				    __snake_case	,	batched=__snake_case	,	remove_columns=["idx", "sentence1", "sentence2"]	,	load_from_cache_file=__snake_case      )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				snake_case__      :Any         =   tokenized_datasets.rename_column("label"	,	"labels"      )

				def collate_fn(__snake_case     :							Dict      ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								if accelerator.distributed_type == DistributedType.TPU:
												return tokenizer.pad(__snake_case	,	padding="max_length"	,	max_length=1_28	,	return_tensors="pt"      )
								return tokenizer.pad(__snake_case	,	padding="longest"	,	return_tensors="pt"      )

				# Instantiate dataloaders.
				snake_case__      :Any         =   DataLoader(
				    tokenized_datasets["train"]	,	shuffle=__snake_case	,	collate_fn=__snake_case	,	batch_size=__snake_case      )
				snake_case__      :Tuple         =   DataLoader(
				    tokenized_datasets["validation"]	,	shuffle=__snake_case	,	collate_fn=__snake_case	,	batch_size=__snake_case      )

				return train_dataloader, eval_dataloader





def 					lowercase_ (    __snake_case     :							List[Any]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							int	,	__snake_case     :							Optional[int]      )     -> Tuple:





				'''simple docstring'''


				model.eval()
				snake_case__      :Union[str, Any]         =   0
				for step, batch in enumerate(__snake_case      ):
								# We could avoid this line since we set the accelerator with `device_placement=True`.
								batch.to(accelerator.device      )
								with torch.no_grad():
												snake_case__      :List[Any]         =   model(**__snake_case      )
								snake_case__      :Any         =   outputs.logits.argmax(dim=-1      )
								# It is slightly faster to call this once, than multiple times
								snake_case__    ,						snake_case__      :Tuple         =   accelerator.gather(
								    (predictions, batch["labels"])      )  # If we are in a multiprocess environment, the last batch has duplicates
								if accelerator.use_distributed:
												if step == len(__snake_case      ) - 1:
																snake_case__      :List[str]         =   predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																snake_case__      :Optional[int]         =   references[: len(eval_dataloader.dataset      ) - samples_seen]
												else:
																samples_seen += references.shape[0]
								metric.add_batch(
								    predictions=__snake_case	,	references=__snake_case	,	)

				snake_case__      :int         =   metric.compute()
				return eval_metric["accuracy"]





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Optional[Any]      )     -> Any:





				'''simple docstring'''


				snake_case__      :Any         =   Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case__      :Union[str, Any]         =   config["lr"]
				snake_case__      :List[str]         =   int(config["num_epochs"]      )
				snake_case__      :Optional[Any]         =   int(config["seed"]      )
				snake_case__      :List[Any]         =   int(config["batch_size"]      )
				snake_case__      :List[Any]         =   args.model_name_or_path

				set_seed(__snake_case      )
				snake_case__    ,						snake_case__      :List[Any]         =   get_dataloaders(__snake_case	,	__snake_case	,	__snake_case      )

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case__      :List[Any]         =   AutoModelForSequenceClassification.from_pretrained(__snake_case	,	return_dict=__snake_case      )

				# Instantiate optimizer
				snake_case__      :int         =   (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				snake_case__      :Tuple         =   optimizer_cls(params=model.parameters()	,	lr=__snake_case      )

				if accelerator.state.deepspeed_plugin is not None:
								snake_case__      :List[str]         =   accelerator.state.deepspeed_plugin.deepspeed_config[
								    "gradient_accumulation_steps"
								]
				else:
								snake_case__      :Any         =   1
				snake_case__      :List[Any]         =   (len(__snake_case      ) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								snake_case__      :Optional[Any]         =   get_linear_schedule_with_warmup(
								    optimizer=__snake_case	,	num_warmup_steps=0	,	num_training_steps=__snake_case	,	)
				else:
								snake_case__      :Any         =   DummyScheduler(__snake_case	,	total_num_steps=__snake_case	,	warmup_num_steps=0      )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :int         =   accelerator.prepare(
				    __snake_case	,	__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )

				# We need to keep track of how many total steps we have iterated over
				snake_case__      :Dict         =   0
				# We also need to keep track of the stating epoch so files are named properly
				snake_case__      :Union[str, Any]         =   0
				snake_case__      :List[str]         =   evaluate.load("glue"	,	"mrpc"      )
				snake_case__      :Optional[Any]         =   num_epochs

				if args.partial_train_epoch is not None:
								snake_case__      :List[Any]         =   args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint      )
								snake_case__      :Union[str, Any]         =   args.resume_from_checkpoint.split("epoch_"      )[1]
								snake_case__      :Dict         =   ""
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								snake_case__      :str         =   int(__snake_case      ) + 1
								snake_case__      :List[Any]         =   evaluation_loop(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
								accelerator.print("resumed checkpoint performance:"	,	__snake_case      )
								accelerator.print("resumed checkpoint's scheduler's lr:"	,	lr_scheduler.get_lr()[0]      )
								accelerator.print("resumed optimizers's lr:"	,	optimizer.param_groups[0]["lr"]      )
								with open(os.path.join(args.output_dir	,	F'state_{starting_epoch-1}.json'      )	,	"r"      ) as f:
												snake_case__      :Tuple         =   json.load(__snake_case      )
												assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["lr"] == lr_scheduler.get_lr()[0]
												), "Scheduler learning rate mismatch, loading from checkpoint failed"
												assert (
												    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
												), "Optimizer learning rate mismatch, loading from checkpoint failed"
												assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
												return

    # Now we train the model
				snake_case__      :Optional[int]         =   {}
				for epoch in range(__snake_case	,	__snake_case      ):
								model.train()
								for step, batch in enumerate(__snake_case      ):
												snake_case__      :str         =   model(**__snake_case      )
												snake_case__      :List[str]         =   outputs.loss
												snake_case__      :List[Any]         =   loss / gradient_accumulation_steps
												accelerator.backward(__snake_case      )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								snake_case__      :int         =   F'epoch_{epoch}'
								snake_case__      :str         =   os.path.join(args.output_dir	,	__snake_case      )
								accelerator.save_state(__snake_case      )
								snake_case__      :Union[str, Any]         =   evaluation_loop(__snake_case	,	__snake_case	,	__snake_case	,	__snake_case      )
								snake_case__      :List[str]         =   accuracy
								snake_case__      :List[str]         =   lr_scheduler.get_lr()[0]
								snake_case__      :List[Any]         =   optimizer.param_groups[0]["lr"]
								snake_case__      :Dict         =   epoch
								snake_case__      :List[Any]         =   overall_step
								accelerator.print(F'epoch {epoch}:'	,	__snake_case      )

								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												with open(os.path.join(args.output_dir	,	F'state_{epoch}.json'      )	,	"w"      ) as f:
																json.dump(__snake_case	,	__snake_case      )





def 					lowercase_ (    )     -> Any:





				'''simple docstring'''


				snake_case__      :List[Any]         =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."      )
				parser.add_argument(
				    "--model_name_or_path"	,	type=__snake_case	,	default="bert-base-cased"	,	help="Path to pretrained model or model identifier from huggingface.co/models."	,	required=__snake_case	,	)
				parser.add_argument(
				    "--output_dir"	,	type=__snake_case	,	default="."	,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."	,	)
				parser.add_argument(
				    "--resume_from_checkpoint"	,	type=__snake_case	,	default=__snake_case	,	help="If the training should continue from a checkpoint folder."	,	)
				parser.add_argument(
				    "--partial_train_epoch"	,	type=__snake_case	,	default=__snake_case	,	help="If passed, the training will stop after this number of epochs."	,	)
				parser.add_argument(
				    "--num_epochs"	,	type=__snake_case	,	default=2	,	help="Number of train epochs."	,	)
				snake_case__      :Any         =   parser.parse_args()
				snake_case__      :int         =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

				training_function(__snake_case	,	__snake_case      )


if __name__ == "__main__":
	main()