
"""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     :			Tuple  						=			16
_A     :			str  						=			32


def     __magic_name__      ( __snake_case							:			Accelerator	,					__snake_case							:			int = 16	,					__snake_case							:			str = "bert-base-cased"    )					->							Union[str, Any]:
       lowercase       :							Tuple									=				AutoTokenizer.from_pretrained(__snake_case    )
       lowercase       :							List[str]									=				load_dataset("glue"	,					"mrpc"    )

       def tokenize_function(__snake_case							:			List[str]    ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase       :							Optional[int]									=				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
       lowercase       :							Optional[Any]									=				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
       lowercase       :							Optional[Any]									=				tokenized_datasets.rename_column("label"	,					"labels"    )

       def collate_fn(__snake_case							:			int    ):
              # 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.
       lowercase       :							Union[str, Any]									=				DataLoader(
           tokenized_datasets["train"]	,					shuffle=__snake_case	,					collate_fn=__snake_case	,					batch_size=__snake_case    )
       lowercase       :							Union[str, Any]									=				DataLoader(
           tokenized_datasets["validation"]	,					shuffle=__snake_case	,					collate_fn=__snake_case	,					batch_size=__snake_case    )

       return train_dataloader, eval_dataloader


def     __magic_name__      ( __snake_case							:			Union[str, Any]	,					__snake_case							:			str    )					->							Union[str, Any]:
       # Initialize accelerator
       lowercase       :							str									=				Accelerator()

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

       set_seed(__snake_case    )
       lowercase						,       lowercase       :							str									=				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)
       lowercase       :							List[Any]									=				AutoModelForSequenceClassification.from_pretrained(__snake_case	,					return_dict=__snake_case    )

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

       if accelerator.state.deepspeed_plugin is not None:
              lowercase       :							Any									=				accelerator.state.deepspeed_plugin.deepspeed_config[
                  "gradient_accumulation_steps"
              ]
       else:
              lowercase       :							int									=				1
       lowercase       :							Dict									=				(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
       ):
              lowercase       :							Tuple									=				get_linear_schedule_with_warmup(
                  optimizer=__snake_case	,					num_warmup_steps=0	,					num_training_steps=__snake_case	,					)
       else:
              lowercase       :							str									=				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.
       lowercase						,       lowercase						,       lowercase						,       lowercase						,       lowercase       :							Tuple									=				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
       lowercase       :							int									=				0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase       :							Optional[int]									=				0

       # Now we train the model
       lowercase       :							List[str]									=				evaluate.load("glue"	,					"mrpc"    )
       lowercase       :							Any									=				0
       lowercase       :							List[Any]									=				{}
       for epoch in range(__snake_case	,					__snake_case    ):
              model.train()
              for step, batch in enumerate(__snake_case    ):
                     lowercase       :							Any									=				model(**__snake_case    )
                     lowercase       :							Dict									=				outputs.loss
                     lowercase       :							Tuple									=				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()
              lowercase       :							Tuple									=				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():
                            lowercase       :							List[Any]									=				model(**__snake_case    )
                     lowercase       :							str									=				outputs.logits.argmax(dim=-1    )
                     # It is slightly faster to call this once, than multiple times
                     lowercase						,       lowercase       :							Any									=				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:
                                   lowercase       :							Any									=				predictions[: len(eval_dataloader.dataset    ) - samples_seen]
                                   lowercase       :							Optional[Any]									=				references[: len(eval_dataloader.dataset    ) - samples_seen]
                            else:
                                   samples_seen += references.shape[0]
                     metric.add_batch(
                         predictions=__snake_case	,					references=__snake_case	,					)

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

              if best_performance < eval_metric["accuracy"]:
                     lowercase       :							Any									=				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     __magic_name__      ( )					->							Dict:
       lowercase       :							List[Any]									=				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."	,					)
       lowercase       :							List[str]									=				parser.parse_args()
       lowercase       :							Optional[int]									=				{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
       training_function(__snake_case	,					__snake_case    )


if __name__ == "__main__":
   main()




"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


