





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case    :       int		       =    {
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case    :       Any		       =    [
         "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
         "AltCLIPPreTrainedModel",
         "AltCLIPModel",
         "AltCLIPTextModel",
         "AltCLIPVisionModel",
     ]


if TYPE_CHECKING:
     from .configuration_altclip import (
         ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
         AltCLIPConfig,
         AltCLIPTextConfig,
         AltCLIPVisionConfig,
     )
     from .processing_altclip import AltCLIPProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_altclip import (
              ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
              AltCLIPModel,
              AltCLIPPreTrainedModel,
              AltCLIPTextModel,
              AltCLIPVisionModel,
          )


else:
     import sys

     _snake_case    :       List[Any]		       =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







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


lowercase__							:	Dict											=		16
lowercase__							:	str											=		32


def 		lowerCamelCase__       (						_A	,      _A = 16	,      _A = "bert-base-cased"				):







	'''simple docstring'''





	snake_case_						       =	AutoTokenizer.from_pretrained(_A				)
	snake_case_						       =	load_dataset("glue"	,      "mrpc"				)

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

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	snake_case_						       =	tokenized_datasets.rename_column("label"	,      "labels"				)

	def collate_fn(_A				):
		# 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(_A	,      padding="max_length"	,      max_length=128	,      return_tensors="pt"				)
		return tokenizer.pad(_A	,      padding="longest"	,      return_tensors="pt"				)

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

	return train_dataloader, eval_dataloader


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	Accelerator()

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

	set_seed(_A				)
	snake_case_       , snake_case_						       =	get_dataloaders(_A	,      _A	,      _A				)

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

	# Instantiate optimizer
	snake_case_						       =	(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	snake_case_						       =	optimizer_cls(params=model.parameters()	,      lr=_A				)

	if accelerator.state.deepspeed_plugin is not None:
		snake_case_						       =	accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		snake_case_						       =	1
	snake_case_						       =	(len(_A				) * 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_						       =	get_linear_schedule_with_warmup(
		    optimizer=_A	,      num_warmup_steps=0	,      num_training_steps=_A	,      )
	else:
		snake_case_						       =	DummyScheduler(_A	,      total_num_steps=_A	,      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_						       =	accelerator.prepare(
	    _A	,      _A	,      _A	,      _A	,      _A				)

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

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

			overall_step += 1

		model.eval()
		snake_case_						       =	0
		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():
				snake_case_						       =	model(**_A				)
			snake_case_						       =	outputs.logits.argmax(dim=-1				)
			# It is slightly faster to call this once, than multiple times
			snake_case_       , snake_case_						       =	accelerator.gather(
			    (predictions, batch["labels"])				)  # If we are in a multiprocess environment, the last batch has duplicates
			if accelerator.use_distributed:
				if step == len(_A				) - 1:
					snake_case_						       =	predictions[: len(eval_dataloader.dataset				) - samples_seen]
					snake_case_						       =	references[: len(eval_dataloader.dataset				) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=_A	,      references=_A	,      )

		snake_case_						       =	metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"epoch {epoch}:"	,      _A				)
		snake_case_						       =	eval_metric["accuracy"]

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


def 		lowerCamelCase__       (						):







	'''simple docstring'''





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


if __name__ == "__main__":
		main()
