






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


__lowerCamelCase      :    List[str]   		=   16
__lowerCamelCase      :    Any   		=   32







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Accelerator  ,						snake_case_      :							int = 16  ,						snake_case_      :							str = "bert-base-cased"     ):
	snake_case__				:   List[Any]     =						AutoTokenizer.from_pretrained(snake_case_     )
	snake_case__				:   Union[str, Any]     =						load_dataset("glue"  ,						"mrpc"     )

	def tokenize_function(snake_case_      :							Optional[int]     ):
		# max_length=None => use the model max length (it's actually the default)
		snake_case__				:   Tuple     =						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
	snake_case__				:   Tuple     =						datasets.map(
	    snake_case_  ,						batched=snake_case_  ,						remove_columns=["idx", "sentence1", "sentence2"]  ,						load_from_cache_file=snake_case_     )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	snake_case__				:   Optional[Any]     =						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.
		if accelerator.distributed_type == DistributedType.TPU:
			return tokenizer.pad(snake_case_  ,						padding="max_length"  ,						max_length=128  ,						return_tensors="pt"     )
		return tokenizer.pad(snake_case_  ,						padding="longest"  ,						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__				:   List[str]     =						DataLoader(
	    tokenized_datasets["validation"]  ,						shuffle=snake_case_  ,						collate_fn=snake_case_  ,						batch_size=snake_case_     )

	return train_dataloader, eval_dataloader







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							int     ):
	# Initialize accelerator
	snake_case__				:   List[str]     =						Accelerator()

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

	set_seed(snake_case_     )
	snake_case__,		snake_case__				:   Optional[Any]     =						get_dataloaders(snake_case_  ,						snake_case_  ,						snake_case_     )

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

	# Instantiate optimizer
	snake_case__				:   Union[str, Any]     =						(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	snake_case__				:   int     =						optimizer_cls(params=model.parameters()  ,						lr=snake_case_     )

	if accelerator.state.deepspeed_plugin is not None:
		snake_case__				:   str     =						accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		snake_case__				:   List[Any]     =						1
	snake_case__				:   Tuple     =						(len(snake_case_     ) * num_epochs) // gradient_accumulation_steps

	# Instantiate scheduler
	if (
	    accelerator.state.deepspeed_plugin is None
	    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
	):
		snake_case__				:   List[str]     =						get_linear_schedule_with_warmup(
		    optimizer=snake_case_  ,						num_warmup_steps=0  ,						num_training_steps=snake_case_  ,						)
	else:
		snake_case__				:   List[Any]     =						DummyScheduler(snake_case_  ,						total_num_steps=snake_case_  ,						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.
	snake_case__,		snake_case__,		snake_case__,		snake_case__,		snake_case__				:   Any     =						accelerator.prepare(
	    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_     )

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

	# Now we train the model
	snake_case__				:   int     =						evaluate.load("glue"  ,						"mrpc"     )
	snake_case__				:   List[Any]     =						0
	snake_case__				:   List[str]     =						{}
	for epoch in range(snake_case_  ,						snake_case_     ):
		model.train()
		for step, batch in enumerate(snake_case_     ):
			snake_case__				:   str     =						model(**snake_case_     )
			snake_case__				:   Optional[Any]     =						outputs.loss
			snake_case__				:   Dict     =						loss / gradient_accumulation_steps
			accelerator.backward(snake_case_     )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

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

		snake_case__				:   Optional[int]     =						metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''  ,						snake_case_     )
		snake_case__				:   str     =						eval_metric["accuracy"]

		if best_performance < eval_metric["accuracy"]:
			snake_case__				:   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(snake_case_  ,						snake_case_     )







def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."     )
	parser.add_argument(
	    "--model_name_or_path"  ,						type=snake_case_  ,						default="bert-base-cased"  ,						help="Path to pretrained model or model identifier from huggingface.co/models."  ,						required=snake_case_  ,						)
	parser.add_argument(
	    "--output_dir"  ,						type=snake_case_  ,						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=snake_case_  ,						default=snake_case_  ,						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=snake_case_  ,						default=3  ,						help="Number of train epochs."  ,						)
	snake_case__				:   List[Any]     =						parser.parse_args()
	snake_case__				:   str     =						{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
	training_function(snake_case_  ,						snake_case_     )


if __name__ == "__main__":
	main()











# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import jax
	import jaxlib

__lowerCamelCase      :    Optional[int]   		=   get_logger()

__lowerCamelCase      :    Optional[dict]   		=   None



class 							SCREAMING_SNAKE_CASE__		(       TensorFormatter[Mapping, "jax.Array", Mapping]		):





					"""simple docstring"""






					def __init__(      self  :						Optional[Any]							,	__A  :						Dict=None							,	__A  :						List[str]=None							,	**__A  :						str ):
						super().__init__(features=__A )
						import jax
						from jaxlib.xla_client import Device

						if isinstance(__A							,	__A ):
							raise ValueError(
							    f'''Expected {device} to be a `str` not {type(__A )}, as `jaxlib.xla_extension.Device` '''
							    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
							    "the device with `str()` to get its string identifier that will be internally mapped "
							    "to the actual `jaxlib.xla_extension.Device`." )
						snake_case__				:   List[Any]     =						device if isinstance(__A							,	__A ) else str(jax.devices()[0] )
						# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
						# with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   Any     =						self._map_devices_to_str()
						if self.device not in list(DEVICE_MAPPING.keys() ):
							logger.warning(
							    f'''Device with string identifier {self.device} not listed among the available '''
							    f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
							    f'''device: {str(jax.devices()[0] )}.''' )
							snake_case__				:   str     =						str(jax.devices()[0] )
						snake_case__				:   str     =						jnp_array_kwargs



					@staticmethod
					def  _lowercase  (      ):
						import jax

						return {str(__A ): device for device in jax.devices()}



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						str ):
						import jax
						import jax.numpy as jnp

						if isinstance(__A							,	__A ) and column:
							if all(
							    isinstance(__A							,	jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
								return jnp.stack(__A							,	axis=0 )
						return column



					def  _lowercase  (      self  :						int							,	__A  :						Tuple ):
						import jax
						import jax.numpy as jnp

						if isinstance(__A							,	(str, bytes, type(__A )) ):
							return value
						elif isinstance(__A							,	(np.character, np.ndarray) ) and np.issubdtype(value.dtype							,	np.character ):
							return value.tolist()

						snake_case__				:   Optional[int]     =						{}

						if isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.integer ):
							# the default int precision depends on the jax config
							# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
							if jax.config.jax_enable_xaa:
								snake_case__				:   Any     =						{"dtype": jnp.intaa}
							else:
								snake_case__				:   Tuple     =						{"dtype": jnp.intaa}
						elif isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.floating ):
							snake_case__				:   str     =						{"dtype": jnp.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
							import PIL.Image

							if isinstance(__A							,	PIL.Image.Image ):
								snake_case__				:   Optional[Any]     =						np.asarray(__A )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   int     =						self._map_devices_to_str()

						with jax.default_device(DEVICE_MAPPING[self.device] ):
							# calling jnp.array on a np.ndarray does copy the data
							# see https://github.com/google/jax/issues/4486
							return jnp.array(__A							,	**{**default_dtype, **self.jnp_array_kwargs} )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[int] ):
						import jax

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
							import torch

							if isinstance(__A							,	torch.Tensor ):
								return self._tensorize(data_struct.detach().cpu().numpy()[()] )
						if hasattr(__A							,	"__array__" ) and not isinstance(__A							,	jax.Array ):
							snake_case__				:   Union[str, Any]     =						data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__A							,	np.ndarray ):
							if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
								return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
						elif isinstance(__A							,	(list, tuple) ):
							return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
						return self._tensorize(__A )



					def  _lowercase  (      self  :						Tuple							,	__A  :						dict ):
						return map_nested(self._recursive_tensorize							,	__A							,	map_list=__A )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						pa.Table ):
						snake_case__				:   int     =						self.numpy_arrow_extractor().extract_row(__A )
						snake_case__				:   Tuple     =						self.python_features_decoder.decode_row(__A )
						return self.recursive_tensorize(__A )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_column(__A )
						snake_case__				:   Optional[int]     =						self.python_features_decoder.decode_column(__A							,	pa_table.column_names[0] )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						snake_case__				:   Dict     =						self._consolidate(__A )
						return column






					def  _lowercase  (      self  :						str							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_batch(__A )
						snake_case__				:   int     =						self.python_features_decoder.decode_batch(__A )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						for column_name in batch:
							snake_case__				:   Any     =						self._consolidate(batch[column_name] )
						return batch



