

import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowerCAmelCase__								=		1_6
lowerCAmelCase__								=		3_2






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Accelerator							,       UpperCAmelCase_							:     int = 1_6	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						AutoTokenizer.from_pretrained('bert-base-cased'	)
	_UpperCamelCase   :		Union[str, Any]			=						load_dataset('glue'							,       'mrpc'	)

	def tokenize_function(UpperCAmelCase_							:     Union[str, Any]	):
		# max_length=None => use the model max length (it's actually the default)
		_UpperCamelCase   :		Optional[int]			=						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
	# starting with the main process first:
	with accelerator.main_process_first():
		_UpperCamelCase   :		Optional[Any]			=						datasets.map(
		    UpperCAmelCase_							,       batched=UpperCAmelCase_							,       remove_columns=['idx', 'sentence1', 'sentence2']							,       )

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

	def collate_fn(UpperCAmelCase_							:     Any	):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		_UpperCamelCase   :		int			=						1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			_UpperCamelCase   :		List[str]			=						1_6
		elif accelerator.mixed_precision != "no":
			_UpperCamelCase   :		List[str]			=						8
		else:
			_UpperCamelCase   :		int			=						None

		return tokenizer.pad(
		    UpperCAmelCase_							,       padding='longest'							,       max_length=UpperCAmelCase_							,       pad_to_multiple_of=UpperCAmelCase_							,       return_tensors='pt'							,       )

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

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							lowerCAmelCase__								=		mocked_dataloaders  # noqa: F811






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     str	)						->				Any:




	'''simple docstring'''




	if os.environ.get('TESTING_MOCKED_DATALOADERS'							,       UpperCAmelCase_	) == "1":
		_UpperCamelCase   :		Any			=						2
	# Initialize Accelerator

	# New Code #
	# We pass in "all" to `log_with` to grab all available trackers in the environment
	# Note: If using a custom `Tracker` class, should be passed in here such as:
	# >>> log_with = ["all", MyCustomTrackerClassInstance()]
	if args.with_tracking:
		_UpperCamelCase   :		Union[str, Any]			=						Accelerator(
		    cpu=args.cpu							,       mixed_precision=args.mixed_precision							,       log_with='all'							,       project_dir=args.project_dir	)
	else:
		_UpperCamelCase   :		Optional[int]			=						Accelerator(cpu=args.cpu							,       mixed_precision=args.mixed_precision	)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	_UpperCamelCase   :		Any			=						config['lr']
	_UpperCamelCase   :		int			=						int(config['num_epochs']	)
	_UpperCamelCase   :		int			=						int(config['seed']	)
	_UpperCamelCase   :		Dict			=						int(config['batch_size']	)
	set_seed(UpperCAmelCase_	)

	_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						get_dataloaders(UpperCAmelCase_							,       UpperCAmelCase_	)
	_UpperCamelCase   :		Dict			=						evaluate.load('glue'							,       'mrpc'	)

	# If the batch size is too big we use gradient accumulation
	_UpperCamelCase   :		Optional[Any]			=						1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		_UpperCamelCase   :		Optional[int]			=						batch_size // MAX_GPU_BATCH_SIZE
		_UpperCamelCase   :		Any			=						MAX_GPU_BATCH_SIZE

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_UpperCamelCase   :		int			=						AutoModelForSequenceClassification.from_pretrained('bert-base-cased'							,       return_dict=UpperCAmelCase_	)

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	_UpperCamelCase   :		Tuple			=						model.to(accelerator.device	)

	# Instantiate optimizer
	_UpperCamelCase   :		int			=						AdamW(params=model.parameters()							,       lr=UpperCAmelCase_	)

	# Instantiate scheduler
	_UpperCamelCase   :		int			=						get_linear_schedule_with_warmup(
	    optimizer=UpperCAmelCase_							,       num_warmup_steps=1_0_0							,       num_training_steps=(len(UpperCAmelCase_	) * num_epochs) // gradient_accumulation_steps							,       )

	# 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   :		Optional[Any]			=						accelerator.prepare(
	    UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_							,       UpperCAmelCase_	)

	# New Code #
	# We need to initialize the trackers we use. Overall configurations can also be stored
	if args.with_tracking:
		_UpperCamelCase   :		Any			=						os.path.split(UpperCAmelCase_	)[-1].split('.'	)[0]
		accelerator.init_trackers(UpperCAmelCase_							,       UpperCAmelCase_	)

	# Now we train the model
	for epoch in range(UpperCAmelCase_	):
		model.train()
		# New Code #
		# For our tracking example, we will log the total loss of each epoch
		if args.with_tracking:
			_UpperCamelCase   :		Optional[int]			=						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	)
			_UpperCamelCase   :		str			=						model(**UpperCAmelCase_	)
			_UpperCamelCase   :		List[str]			=						outputs.loss
			# New Code #
			if args.with_tracking:
				total_loss += loss.detach().float()
			_UpperCamelCase   :		int			=						loss / gradient_accumulation_steps
			accelerator.backward(UpperCAmelCase_	)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(UpperCAmelCase_	):
			# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
			batch.to(accelerator.device	)
			with torch.no_grad():
				_UpperCamelCase   :		List[str]			=						model(**UpperCAmelCase_	)
			_UpperCamelCase   :		List[Any]			=						outputs.logits.argmax(dim=-1	)
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						accelerator.gather_for_metrics((predictions, batch['labels'])	)
			metric.add_batch(
			    predictions=UpperCAmelCase_							,       references=UpperCAmelCase_							,       )

		_UpperCamelCase   :		Union[str, Any]			=						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''							,       UpperCAmelCase_	)

		# New Code #
		# To actually log, we call `Accelerator.log`
		# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
		if args.with_tracking:
			accelerator.log(
			    {
			        'accuracy': eval_metric['accuracy'],
			        'f1': eval_metric['f1'],
			        'train_loss': total_loss.item() / len(UpperCAmelCase_	),
			        'epoch': epoch,
			    }							,       step=UpperCAmelCase_							,       )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
	if args.with_tracking:
		accelerator.end_training()






def 					lowerCamelCase_     (							)						->				List[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						argparse.ArgumentParser(description='Simple example of training script.'	)
	parser.add_argument(
	    '--mixed_precision'							,       type=UpperCAmelCase_							,       default=UpperCAmelCase_							,       choices=['no', 'fp16', 'bf16', 'fp8']							,       help='Whether to use mixed precision. Choose'
	    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
	    'and an Nvidia Ampere GPU.'							,       )
	parser.add_argument('--cpu'							,       action='store_true'							,       help='If passed, will train on the CPU.'	)
	parser.add_argument(
	    '--with_tracking'							,       action='store_true'							,       help='Whether to load in all available experiment trackers from the environment and use them for logging.'							,       )
	parser.add_argument(
	    '--project_dir'							,       type=UpperCAmelCase_							,       default='logs'							,       help='Location on where to store experiment tracking logs` and relevent project information'							,       )
	_UpperCamelCase   :		Dict			=						parser.parse_args()
	_UpperCamelCase   :		Tuple			=						{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
	training_function(UpperCAmelCase_							,       UpperCAmelCase_	)


if __name__ == "__main__":
							main()







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 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]	)						->				Optional[int]:




	'''simple docstring'''




	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		_UpperCamelCase   :		List[Any]			=						k.replace(UpperCAmelCase_							,       UpperCAmelCase_	)

	if k.startswith('encoder'	):
		_UpperCamelCase   :		Optional[Any]			=						k.replace('.attn'							,       '.self_attn'	)
		_UpperCamelCase   :		Optional[int]			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'final_layer_norm'	)
	elif k.startswith('decoder'	):
		_UpperCamelCase   :		Any			=						k.replace('norm1'							,       'self_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm2'							,       'encoder_attn_layer_norm'	)
		_UpperCamelCase   :		Tuple			=						k.replace('norm3'							,       'final_layer_norm'	)
	return k






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Optional[Any]:




	'''simple docstring'''




	_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   :		str			=						k.replace('layernorm_embedding'							,       'layer_norm'	)
		assert new_k not in sd
		_UpperCamelCase   :		Tuple			=						v


lowerCAmelCase__								=		["""START"""]






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     Optional[Any]							,       UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						torch.load(UpperCAmelCase_							,       map_location='cpu'	)
	_UpperCamelCase   :		int			=						model['model']
	_UpperCamelCase   :		List[Any]			=						BlenderbotConfig.from_json_file(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						BlenderbotForConditionalGeneration(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						m.model.state_dict().keys()
	_UpperCamelCase   :		Union[str, Any]			=						[]
	_UpperCamelCase   :		int			=						{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		_UpperCamelCase   :		Optional[int]			=						rename_state_dict_key(UpperCAmelCase_	)
		if new_k not in valid_keys:
			failures.append([k, new_k]	)
		else:
			_UpperCamelCase   :		int			=						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)




