


"""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
# and perform 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
#
########################################################################


SCREAMING_SNAKE_CASE :    Optional[Any]									=				16
SCREAMING_SNAKE_CASE :    Optional[Any]									=				32






def 		lowercase							(     _snake_case						:      Accelerator     ,   _snake_case						:      int = 16     )     ->int:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				AutoTokenizer.from_pretrained('''bert-base-cased'''     )
	__snake_case	:						Any						=				load_dataset('''glue'''     ,   '''mrpc'''     )

	def tokenize_function(_snake_case						:      Dict     ):
		# max_length=None => use the model max length (it's actually the default)
		__snake_case	:						List[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
	# starting with the main process first:
	with accelerator.main_process_first():
		__snake_case	:						Tuple						=				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
	__snake_case	:						int						=				tokenized_datasets.rename_column('''label'''     ,   '''labels'''     )

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

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

	# Instantiate dataloaders.
	__snake_case	:						Union[str, Any]						=				DataLoader(
	    tokenized_datasets['''train''']     ,   shuffle=_snake_case     ,   collate_fn=_snake_case     ,   batch_size=_snake_case     )
	__snake_case	:						str						=				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

			SCREAMING_SNAKE_CASE :    str									=				mocked_dataloaders  # noqa: F811






def 		lowercase							(     _snake_case						:      Dict     ,   _snake_case						:      List[str]     )     ->Optional[int]:
	"""simple docstring"""





	if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,   _snake_case     ) == "1":
		__snake_case	:						List[str]						=				2
	# New Code #
	__snake_case	:						int						=				int(args.gradient_accumulation_steps     )
	# Initialize accelerator
	__snake_case	:						List[str]						=				Accelerator(
	    cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   gradient_accumulation_steps=_snake_case     )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''     )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	__snake_case	:						Any						=				config['''lr''']
	__snake_case	:						Optional[int]						=				int(config['''num_epochs''']     )
	__snake_case	:						Union[str, Any]						=				int(config['''seed''']     )
	__snake_case	:						Dict						=				int(config['''batch_size''']     )

	__snake_case	:						Any						=				evaluate.load('''glue'''     ,   '''mrpc'''     )

	set_seed(_snake_case     )
	__snake_case	,							__snake_case	:						Dict						=				get_dataloaders(_snake_case     ,   _snake_case     )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	__snake_case	:						Dict						=				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).
	__snake_case	:						Any						=				model.to(accelerator.device     )

	# Instantiate optimizer
	__snake_case	:						Any						=				AdamW(params=model.parameters()     ,   lr=_snake_case     )

	# Instantiate scheduler
	__snake_case	:						Any						=				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.
	__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	,							__snake_case	:						int						=				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()
		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     ):
				__snake_case	:						Union[str, Any]						=				model(**_snake_case     )
				__snake_case	:						Optional[int]						=				output.loss
				accelerator.backward(_snake_case     )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		__snake_case	:						Any						=				metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""     ,   _snake_case     )






def 		lowercase							(     )     ->List[Any]:
	"""simple docstring"""





	__snake_case	:						Dict						=				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('''--cpu'''     ,   action='''store_true'''     ,   help='''If passed, will train on the CPU.'''     )
	__snake_case	:						List[str]						=				parser.parse_args()
	__snake_case	:						Union[str, Any]						=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_snake_case     ,   _snake_case     )


if __name__ == "__main__":
			main()









"""simple docstring"""
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      int     )     ->List[Any]:
	"""simple docstring"""





	if openai_config_file == "":
		__snake_case	:						Dict						=				OpenAIGPTConfig()
	else:
		__snake_case	:						int						=				OpenAIGPTConfig.from_json_file(_snake_case     )
	__snake_case	:						Tuple						=				OpenAIGPTModel(_snake_case     )

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(_snake_case     ,   _snake_case     ,   _snake_case     )

	# Save pytorch-model
	__snake_case	:						str						=				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
	__snake_case	:						Optional[Any]						=				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""     )
	torch.save(model.state_dict()     ,   _snake_case     )
	print(f"""Save configuration file to {pytorch_config_dump_path}"""     )
	with open(_snake_case     ,   '''w'''     ,   encoding='''utf-8'''     ) as f:
		f.write(config.to_json_string()     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Any									=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--openai_checkpoint_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the TensorFlow checkpoint path.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			parser.add_argument(
			    """--openai_config_file""",
			    default="""""",
			    type=str,
			    help=(
			        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()
			convert_openai_checkpoint_to_pytorch(
			    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
			)





