
'''simple docstring'''




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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


__lowercase				   =  1_6
__lowercase				   =  3_2






def     snake_case__							(      _A: Any				,       _A: Any			)   ->     Union[str, Any]:

		'''simple docstring'''


		if args.with_tracking:
				lowerCAmelCase        =  Accelerator(
				    cpu=args.cpu				,       mixed_precision=args.mixed_precision				,       log_with="""all"""				,       project_dir=args.project_dir			)
		else:
				lowerCAmelCase        =  Accelerator(cpu=args.cpu				,       mixed_precision=args.mixed_precision			)

		if hasattr(args.checkpointing_steps				,       """isdigit"""			):
				if args.checkpointing_steps == "epoch":
						lowerCAmelCase        =  args.checkpointing_steps
				elif args.checkpointing_steps.isdigit():
						lowerCAmelCase        =  int(args.checkpointing_steps			)
				else:
						raise ValueError(
						    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."			)
		else:
				lowerCAmelCase        =  None
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase        =  config["""lr"""]
		lowerCAmelCase        =  int(config["""num_epochs"""]			)
		lowerCAmelCase        =  int(config["""seed"""]			)
		lowerCAmelCase        =  int(config["""batch_size"""]			)

		# We need to initialize the trackers we use, and also store our configuration
		if args.with_tracking:
				lowerCAmelCase        =  os.path.split(_A			)[-1].split("""."""			)[0]
				accelerator.init_trackers(_A				,       _A			)

		lowerCAmelCase        =  AutoTokenizer.from_pretrained("""bert-base-cased"""			)
		lowerCAmelCase        =  load_dataset("""glue"""				,       """mrpc"""			)
		lowerCAmelCase        =  evaluate.load("""glue"""				,       """mrpc"""			)

		def tokenize_function(_A: List[Any]			):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase        =  tokenizer(examples["""sentence1"""]				,       examples["""sentence2"""]				,       truncation=_A				,       max_length=_A			)
				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():
				lowerCAmelCase        =  datasets.map(
				    _A				,       batched=_A				,       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
		lowerCAmelCase        =  tokenized_datasets.rename_column("""label"""				,       """labels"""			)

		# If the batch size is too big we use gradient accumulation
		lowerCAmelCase        =  1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				lowerCAmelCase        =  batch_size // MAX_GPU_BATCH_SIZE
				lowerCAmelCase        =  MAX_GPU_BATCH_SIZE

		def collate_fn(_A: Any			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowerCAmelCase        =  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":
						lowerCAmelCase        =  16
				elif accelerator.mixed_precision != "no":
						lowerCAmelCase        =  8
				else:
						lowerCAmelCase        =  None

				return tokenizer.pad(
				    _A				,       padding="""longest"""				,       max_length=_A				,       pad_to_multiple_of=_A				,       return_tensors="""pt"""				,       )

		# Instantiate dataloaders.
		lowerCAmelCase        =  DataLoader(
		    tokenized_datasets["""train"""]				,       shuffle=_A				,       collate_fn=_A				,       batch_size=_A			)
		lowerCAmelCase        =  DataLoader(
		    tokenized_datasets["""validation"""]				,       shuffle=_A				,       collate_fn=_A				,       batch_size=_A			)

		set_seed(_A			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase        =  AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,       return_dict=_A			)

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

		# Instantiate optimizer
		lowerCAmelCase        =  AdamW(params=model.parameters()				,       lr=_A			)

		# Instantiate scheduler
		lowerCAmelCase        =  get_linear_schedule_with_warmup(
		    optimizer=_A				,       num_warmup_steps=100				,       num_training_steps=(len(_A			) * 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.
		lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase ,					lowerCAmelCase        =  accelerator.prepare(
		    _A				,       _A				,       _A				,       _A				,       _A			)

		# We need to keep track of how many total steps we have iterated over
		lowerCAmelCase        =  0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCAmelCase        =  0

		# Potentially 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			)
						lowerCAmelCase        =  os.path.basename(args.resume_from_checkpoint			)
				else:
						# Get the most recent checkpoint
						lowerCAmelCase        =  [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
						dirs.sort(key=os.path.getctime			)
						lowerCAmelCase        =  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract `epoch_{i}` or `step_{i}`
				lowerCAmelCase        =  os.path.splitext(_A			)[0]

				if "epoch" in training_difference:
						lowerCAmelCase        =  int(training_difference.replace("""epoch_"""				,       """"""			)			) + 1
						lowerCAmelCase        =  None
				else:
						lowerCAmelCase        =  int(training_difference.replace("""step_"""				,       """"""			)			)
						lowerCAmelCase        =  resume_step // len(_A			)
						resume_step -= starting_epoch * len(_A			)

    # Now we train the model
		for epoch in range(_A				,       _A			):
				model.train()
				if args.with_tracking:
						lowerCAmelCase        =  0
				if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
						# We need to skip steps until we reach the resumed step
						lowerCAmelCase        =  accelerator.skip_first_batches(_A				,       _A			)
						overall_step += resume_step
				else:
						# After the first iteration though, we need to go back to the original dataloader
						lowerCAmelCase        =  train_dataloader
				for step, batch in enumerate(_A			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						lowerCAmelCase        =  model(**_A			)
						lowerCAmelCase        =  outputs.loss
						lowerCAmelCase        =  loss / gradient_accumulation_steps
						# We keep track of the loss at each epoch
						if args.with_tracking:
								total_loss += loss.detach().float()
						accelerator.backward(_A			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

						if isinstance(_A				,       _A			):
								lowerCAmelCase        =  f"step_{overall_step}"
								if overall_step % checkpointing_steps == 0:
										if args.output_dir is not None:
												lowerCAmelCase        =  os.path.join(args.output_dir				,       _A			)
										accelerator.save_state(_A			)

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

				lowerCAmelCase        =  metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f"epoch {epoch}:"				,       _A			)
				if args.with_tracking:
						accelerator.log(
						    {
						        """accuracy""": eval_metric["""accuracy"""],
						        """f1""": eval_metric["""f1"""],
						        """train_loss""": total_loss.item() / len(_A			),
						        """epoch""": epoch,
						    }				,       step=_A				,       )

				if checkpointing_steps == "epoch":
						lowerCAmelCase        =  f"epoch_{epoch}"
						if args.output_dir is not None:
								lowerCAmelCase        =  os.path.join(args.output_dir				,       _A			)
						accelerator.save_state(_A			)

		if args.with_tracking:
				accelerator.end_training()






def     snake_case__							(      )   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  argparse.ArgumentParser(description="""Simple example of training script."""			)
		parser.add_argument(
		    """--mixed_precision"""				,       type=_A				,       default=_A				,       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(
		    """--checkpointing_steps"""				,       type=_A				,       default=_A				,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""				,       )
		parser.add_argument(
		    """--resume_from_checkpoint"""				,       type=_A				,       default=_A				,       help="""If the training should continue from a checkpoint folder."""				,       )
		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(
		    """--output_dir"""				,       type=_A				,       default="""."""				,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,       )
		parser.add_argument(
		    """--project_dir"""				,       type=_A				,       default="""logs"""				,       help="""Location on where to store experiment tracking logs` and relevent project information"""				,       )
		lowerCAmelCase        =  parser.parse_args()
		lowerCAmelCase        =  {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(_A				,       _A			)


if __name__ == "__main__":
						main()





'''simple docstring'''




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()






def     snake_case__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

		# Embeddings
		# Position IDs
		lowerCAmelCase        =  fnet_pretraining_model.state_dict()

		lowerCAmelCase        =  state_dict["""fnet.embeddings.position_ids"""]
		lowerCAmelCase        =  {"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

		# Encoder Layers
		for layer in range(config.num_hidden_layers			):
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

		# Pooler Layers
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_bias"""]			)

		# Load State Dict
		fnet_pretraining_model.load_state_dict(_A			)

		# Save PreTrained
		print(f"Saving pretrained model to {save_path}"			)
		fnet_pretraining_model.save_pretrained(_A			)


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--flax_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--fnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained FNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument('''--save_path''', default=None, type=str, required=True, help='''Path to the output model.''')
						__lowercase				   =  parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




