

'''simple docstring'''


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


lowerCAmelCase__										= 16
lowerCAmelCase__										= 32







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase = 1_6   ,UpperCamelCase = "bert-base-cased"  )    ->    Tuple:
	UpperCAmelCase_		:      Optional[Any]       	=					AutoTokenizer.from_pretrained(UpperCamelCase  )
	UpperCAmelCase_		:      Dict       	=					load_dataset('glue'   ,'mrpc'  )

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

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

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

	def collate_fn(UpperCamelCase  ):
		# 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(UpperCamelCase   ,padding='max_length'   ,max_length=1_2_8   ,return_tensors='pt'  )
		return tokenizer.pad(UpperCamelCase   ,padding='longest'   ,return_tensors='pt'  )

	# Instantiate dataloaders.
	UpperCAmelCase_		:      str       	=					DataLoader(
	    tokenized_datasets['train']   ,shuffle=UpperCamelCase   ,collate_fn=UpperCamelCase   ,batch_size=UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					DataLoader(
	    tokenized_datasets['validation']   ,shuffle=UpperCamelCase   ,collate_fn=UpperCamelCase   ,batch_size=UpperCamelCase  )

	return train_dataloader, eval_dataloader







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

	UpperCAmelCase_		:      Dict       	=					metric.compute()
	return eval_metric["accuracy"]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	# Initialize accelerator
	UpperCAmelCase_		:      Optional[int]       	=					Accelerator()

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

	set_seed(UpperCamelCase  )
	UpperCAmelCase_	,    UpperCAmelCase_		:      str       	=					get_dataloaders(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_		:      Dict       	=					AutoModelForSequenceClassification.from_pretrained(UpperCamelCase   ,return_dict=UpperCamelCase  )

	# Instantiate optimizer
	UpperCAmelCase_		:      List[str]       	=					(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	UpperCAmelCase_		:      Tuple       	=					optimizer_cls(params=model.parameters()   ,lr=UpperCamelCase  )

	if accelerator.state.deepspeed_plugin is not None:
		UpperCAmelCase_		:      str       	=					accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		UpperCAmelCase_		:      Optional[Any]       	=					1
	UpperCAmelCase_		:      Union[str, Any]       	=					(len(UpperCamelCase  ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		UpperCAmelCase_		:      Optional[int]       	=					get_linear_schedule_with_warmup(
		    optimizer=UpperCamelCase   ,num_warmup_steps=0   ,num_training_steps=UpperCamelCase   ,)
	else:
		UpperCAmelCase_		:      Union[str, Any]       	=					DummyScheduler(UpperCamelCase   ,total_num_steps=UpperCamelCase   ,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.
	UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					accelerator.prepare(
	    UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )

	# We need to keep track of how many total steps we have iterated over
	UpperCAmelCase_		:      Dict       	=					0
	# We also need to keep track of the stating epoch so files are named properly
	UpperCAmelCase_		:      int       	=					0
	UpperCAmelCase_		:      str       	=					evaluate.load('glue'   ,'mrpc'  )
	UpperCAmelCase_		:      str       	=					num_epochs

	if args.partial_train_epoch is not None:
		UpperCAmelCase_		:      str       	=					args.partial_train_epoch

	if args.resume_from_checkpoint:
		accelerator.load_state(args.resume_from_checkpoint  )
		UpperCAmelCase_		:      Union[str, Any]       	=					args.resume_from_checkpoint.split('epoch_'  )[1]
		UpperCAmelCase_		:      int       	=					''
		for char in epoch_string:
			if char.isdigit():
				state_epoch_num += char
			else:
				break
		UpperCAmelCase_		:      Tuple       	=					int(UpperCamelCase  ) + 1
		UpperCAmelCase_		:      Tuple       	=					evaluation_loop(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )
		accelerator.print('resumed checkpoint performance:'   ,UpperCamelCase  )
		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:
			UpperCAmelCase_		:      Dict       	=					json.load(UpperCamelCase  )
			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
	UpperCAmelCase_		:      Any       	=					{}
	for epoch in range(UpperCamelCase   ,UpperCamelCase  ):
		model.train()
		for step, batch in enumerate(UpperCamelCase  ):
			UpperCAmelCase_		:      List[Any]       	=					model(**UpperCamelCase  )
			UpperCAmelCase_		:      Any       	=					outputs.loss
			UpperCAmelCase_		:      Any       	=					loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase  )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		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(UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      str       	=					argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
	parser.add_argument(
	    '--model_name_or_path'   ,type=UpperCamelCase   ,default='bert-base-cased'   ,help='Path to pretrained model or model identifier from huggingface.co/models.'   ,required=UpperCamelCase   ,)
	parser.add_argument(
	    '--output_dir'   ,type=UpperCamelCase   ,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=UpperCamelCase   ,default=UpperCamelCase   ,help='If the training should continue from a checkpoint folder.'   ,)
	parser.add_argument(
	    '--partial_train_epoch'   ,type=UpperCamelCase   ,default=UpperCamelCase   ,help='If passed, the training will stop after this number of epochs.'   ,)
	parser.add_argument(
	    '--num_epochs'   ,type=UpperCamelCase   ,default=2   ,help='Number of train epochs.'   ,)
	UpperCAmelCase_		:      Tuple       	=					parser.parse_args()
	UpperCAmelCase_		:      List[Any]       	=					{'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 4_2, 'batch_size': 1_6}

	training_function(UpperCamelCase   ,UpperCamelCase  )


if __name__ == "__main__":
						main()









'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Union[str, Any]       	=					[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		UpperCAmelCase_		:      Optional[int]       	=					sd.pop(UpperCamelCase  )
		UpperCAmelCase_		:      List[Any]       	=					k.replace('layernorm_embedding'   ,'layer_norm'  )
		assert new_k not in sd
		UpperCAmelCase_		:      Any       	=					v


lowerCAmelCase__										= ["START"]







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      str       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
	UpperCAmelCase_		:      Tuple       	=					model['model']
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotConfig.from_json_file(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotForConditionalGeneration(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					m.model.state_dict().keys()
	UpperCAmelCase_		:      Optional[Any]       	=					[]
	UpperCAmelCase_		:      Dict       	=					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		UpperCAmelCase_		:      Union[str, Any]       	=					rename_state_dict_key(UpperCamelCase  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			UpperCAmelCase_		:      List[str]       	=					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase  )
	m.model.load_state_dict(UpperCamelCase   ,strict=UpperCamelCase  )
	m.half()
	m.save_pretrained(UpperCamelCase  )


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
						parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
						parser.add_argument(
						    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
						)
						lowerCAmelCase__										= parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






