


'''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


__A	=16
__A	=32







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = 1_6 ,      UpperCamelCase__ = "bert-base-cased"					):
			UpperCAmelCase__  :					str        =     AutoTokenizer.from_pretrained(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     load_dataset("""glue""" ,      """mrpc"""					)

			def tokenize_function(UpperCamelCase__					):
						# 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
			UpperCAmelCase__  :					Optional[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__  :					Optional[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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			# Initialize accelerator
			UpperCAmelCase__  :					Union[str, Any]        =     Accelerator()

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

			set_seed(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     get_dataloaders(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

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

			# Instantiate optimizer
			UpperCAmelCase__  :					Union[str, Any]        =     (
			    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__  :					List[Any]        =     accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						UpperCAmelCase__  :					int        =     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__  :					Optional[int]        =     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__  :					Tuple        =     accelerator.prepare(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase__  :					Union[str, Any]        =     0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase__  :					Optional[Any]        =     0

			# Now we train the model
			UpperCAmelCase__  :					int        =     evaluate.load("""glue""" ,      """mrpc"""					)
			UpperCAmelCase__  :					Dict        =     0
			UpperCAmelCase__  :					Tuple        =     {}
			for epoch in range(UpperCamelCase__ ,      UpperCamelCase__					):
						model.train()
						for step, batch in enumerate(UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     model(**UpperCamelCase__					)
									UpperCAmelCase__  :					int        =     outputs.loss
									UpperCAmelCase__  :					int        =     loss / gradient_accumulation_steps
									accelerator.backward(UpperCamelCase__					)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

						model.eval()
						UpperCAmelCase__  :					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__  :					Dict        =     model(**UpperCamelCase__					)
									UpperCAmelCase__  :					Optional[int]        =     outputs.logits.argmax(dim=-1					)
									# It is slightly faster to call this once, than multiple times
									UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     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__  :					str        =     predictions[: len(eval_dataloader.dataset					) - samples_seen]
															UpperCAmelCase__  :					Union[str, Any]        =     references[: len(eval_dataloader.dataset					) - samples_seen]
												else:
															samples_seen += references.shape[0]
									metric.add_batch(
									    predictions=UpperCamelCase__ ,      references=UpperCamelCase__ ,      )

						UpperCAmelCase__  :					Optional[int]        =     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'''epoch {epoch}:''' ,      UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     eval_metric["""accuracy"""]

						if best_performance < eval_metric["accuracy"]:
									UpperCAmelCase__  :					List[str]        =     eval_metric["""accuracy"""]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir ,      """all_results.json"""					) ,      """w"""					) as f:
									json.dump(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		):
			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(
			    """--performance_lower_bound""" ,      type=UpperCamelCase__ ,      default=UpperCamelCase__ ,      help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.""" ,      )
			parser.add_argument(
			    """--num_epochs""" ,      type=UpperCamelCase__ ,      default=3 ,      help="""Number of train epochs.""" ,      )
			UpperCAmelCase__  :					str        =     parser.parse_args()
			UpperCAmelCase__  :					Optional[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 re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
			if (
			    any("""norm""" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__  :					Any        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						UpperCAmelCase__  :					Any        =     pt_tensor.transpose(2 ,      3 ,      1 ,      0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__  :					Optional[Any]        =     pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			UpperCAmelCase__  :					str        =     flax_model.init_weights(PRNGKey(UpperCamelCase__					)					)

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     rename_key_and_reshape_tensor(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)