

from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


_lowerCAmelCase					:			List[Any]  				=			logging.get_logger(__name__)






def        UpperCamelCase_(							_snake_case   :  List[Any]       ,     _snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			try:
						with open(_snake_case       ,     'rb'	) as flax_state_f:
									__a        =from_bytes(_snake_case       ,     flax_state_f.read()	)
			except UnpicklingError as e:
						try:
									with open(_snake_case	) as f:
												if f.read().startswith('version'	):
															raise OSError(
															    'You seem to have cloned a repository without having git-lfs installed. Please'
															    ' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'
															    ' folder you cloned.'	)
												else:
															raise ValueError from e
						except (UnicodeDecodeError, ValueError):
									raise EnvironmentError(F'Unable to convert {model_file} to Flax deserializable object. '	)

			return load_flax_weights_in_pytorch_model(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Optional[int]       ,     _snake_case   :  str	):




			"""simple docstring"""






			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    'Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'
						    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
						    ' instructions.'	)
						raise

			# check if we have bf16 weights
			__a        =flatten_dict(jax.tree_util.tree_map(lambda _snake_case	: x.dtype == jnp.bfloataa       ,     _snake_case	)	).values()
			if any(_snake_case	):
						# convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
						    'before loading those in PyTorch model.'	)
						__a        =jax.tree_util.tree_map(
						    lambda _snake_case	: params.astype(np.floataa	) if params.dtype == jnp.bfloataa else params       ,     _snake_case	)

			__a        =''

			__a        =flatten_dict(_snake_case       ,     sep='.'	)
			__a        =pt_model.state_dict()

			# keep track of unexpected & missing keys
			__a        =[]
			__a        =set(pt_model_dict.keys()	)

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						__a        =flax_key_tuple.split('.'	)

						if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
									__a        =flax_key_tuple_array[:-1] + ['weight']
									__a        =jnp.transpose(_snake_case       ,     (3, 2, 0, 1)	)
						elif flax_key_tuple_array[-1] == "kernel":
									__a        =flax_key_tuple_array[:-1] + ['weight']
									__a        =flax_tensor.T
						elif flax_key_tuple_array[-1] == "scale":
									__a        =flax_key_tuple_array[:-1] + ['weight']

						if "time_embedding" not in flax_key_tuple_array:
									for i, flax_key_tuple_string in enumerate(_snake_case	):
												__a        =(
												    flax_key_tuple_string.replace('_0'       ,     '.0'	)
												    .replace('_1'       ,     '.1'	)
												    .replace('_2'       ,     '.2'	)
												    .replace('_3'       ,     '.3'	)
												    .replace('_4'       ,     '.4'	)
												    .replace('_5'       ,     '.5'	)
												    .replace('_6'       ,     '.6'	)
												    .replace('_7'       ,     '.7'	)
												    .replace('_8'       ,     '.8'	)
												    .replace('_9'       ,     '.9'	)
												)

						__a        ='.'.join(_snake_case	)

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    F'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
												    F'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'	)
									else:
												# add weight to pytorch dict
												__a        =np.asarray(_snake_case	) if not isinstance(_snake_case       ,     np.ndarray	) else flax_tensor
												__a        =torch.from_numpy(_snake_case	)
												# remove from missing keys
												missing_keys.remove(_snake_case	)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(_snake_case	)

			pt_model.load_state_dict(_snake_case	)

			# re-transform missing_keys to list
			__a        =list(_snake_case	)

			if len(_snake_case	) > 0:
						logger.warning(
						    'Some weights of the Flax model were not used when initializing the PyTorch model'
						    F' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
						    F' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
						    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
						    F' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
						    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
						    ' FlaxBertForSequenceClassification model).'	)
			if len(_snake_case	) > 0:
						logger.warning(
						    F'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
						    F' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
						    ' use it for predictions and inference.'	)

			return pt_model









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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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					:			Union[str, Any]  				=			16
_lowerCAmelCase					:			List[str]  				=			32






def        UpperCamelCase_(							_snake_case   :  Accelerator       ,     _snake_case   :  int = 16	):




			"""simple docstring"""






			__a        =AutoTokenizer.from_pretrained('bert-base-cased'	)
			__a        =load_dataset('glue'       ,     'mrpc'	)

			def tokenize_function(_snake_case   :  Optional[int]	):
						# max_length=None => use the model max length (it's actually the default)
						__a        =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
			# starting with the main process first:
			with accelerator.main_process_first():
						__a        =datasets.map(
						    _snake_case       ,     batched=_snake_case       ,     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
			__a        =tokenized_datasets.rename_column('label'       ,     'labels'	)

			def collate_fn(_snake_case   :  List[Any]	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__a        =128 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									__a        =16
						elif accelerator.mixed_precision != "no":
									__a        =8
						else:
									__a        =None

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

			# Instantiate dataloaders.
			__a        =DataLoader(
			    tokenized_datasets['train']       ,     shuffle=_snake_case       ,     collate_fn=_snake_case       ,     batch_size=_snake_case	)
			__a        =DataLoader(
			    tokenized_datasets['validation']       ,     shuffle=_snake_case       ,     collate_fn=_snake_case       ,     batch_size=_snake_case	)

			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					:			List[Any]  				=			mocked_dataloaders  # noqa: F811






def        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			if os.environ.get('TESTING_MOCKED_DATALOADERS'       ,     _snake_case	) == "1":
						__a        =2
			# New Code #
			__a        =int(args.gradient_accumulation_steps	)
			__a        =int(args.local_sgd_steps	)
			# Initialize accelerator
			__a        =Accelerator(
			    cpu=args.cpu       ,     mixed_precision=args.mixed_precision       ,     gradient_accumulation_steps=_snake_case	)
			if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
						raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'	)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a        =config['lr']
			__a        =int(config['num_epochs']	)
			__a        =int(config['seed']	)
			__a        =int(config['batch_size']	)

			__a        =evaluate.load('glue'       ,     'mrpc'	)

			set_seed(_snake_case	)
			__a      ,    __a        =get_dataloaders(_snake_case       ,     _snake_case	)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a        =AutoModelForSequenceClassification.from_pretrained('bert-base-cased'       ,     return_dict=_snake_case	)

			# 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).
			__a        =model.to(accelerator.device	)

			# Instantiate optimizer
			__a        =AdamW(params=model.parameters()       ,     lr=_snake_case	)

			# Instantiate scheduler
			__a        =get_linear_schedule_with_warmup(
			    optimizer=_snake_case       ,     num_warmup_steps=100       ,     num_training_steps=(len(_snake_case	) * num_epochs)       ,     )

			# 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.
			__a      ,    __a      ,    __a      ,    __a      ,    __a        =accelerator.prepare(
			    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)

			# Now we train the model
			for epoch in range(_snake_case	):
						model.train()
						with LocalSGD(
						    accelerator=_snake_case       ,     model=_snake_case       ,     local_sgd_steps=_snake_case       ,     enabled=local_sgd_steps is not None	) as local_sgd:
									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	)
												# New code #
												# We use the new `accumulate` context manager to perform gradient accumulation
												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
												with accelerator.accumulate(_snake_case	):
															__a        =model(**_snake_case	)
															__a        =output.loss
															accelerator.backward(_snake_case	)
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()
															# LocalSGD-specific line
															local_sgd.step()

						model.eval()
						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():
												__a        =model(**_snake_case	)
									__a        =outputs.logits.argmax(dim=-1	)
									__a      ,    __a        =accelerator.gather_for_metrics((predictions, batch['labels'])	)
									metric.add_batch(
									    predictions=_snake_case       ,     references=_snake_case       ,     )

						__a        =metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'epoch {epoch}:'       ,     _snake_case	)






def        UpperCamelCase_(							):




			"""simple docstring"""






			__a        =argparse.ArgumentParser(description='Simple example of training script.'	)
			parser.add_argument(
			    '--mixed_precision'       ,     type=_snake_case       ,     default=_snake_case       ,     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.'       ,     )
			# New Code #
			parser.add_argument(
			    '--gradient_accumulation_steps'       ,     type=_snake_case       ,     default=1       ,     help='The number of minibatches to be ran before gradients are accumulated.'       ,     )
			parser.add_argument(
			    '--local_sgd_steps'       ,     type=_snake_case       ,     default=8       ,     help='Number of local SGD steps or None to disable local SGD'	)
			parser.add_argument('--cpu'       ,     action='store_true'       ,     help='If passed, will train on the CPU.'	)
			__a        =parser.parse_args()
			__a        ={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_snake_case       ,     _snake_case	)


if __name__ == "__main__":
						main()






