


"""simple docstring"""


import argparse

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
#
# 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
#
########################################################################


A     :   Dict			    =			1_6
A     :   Dict			    =			3_2


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase = 16      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			AutoTokenizer.from_pretrained("bert-base-cased"      )
			__lowerCAmelCase				    =			load_dataset("glue"    , "mrpc"      )

			def tokenize_function(_UpperCamelCase      ):
						# max_length=None => use the model max length (it's actually the default)
						__lowerCAmelCase				    =			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
			# starting with the main process first:
			with accelerator.main_process_first():
						__lowerCAmelCase				    =			datasets.map(
						    _UpperCamelCase    , batched=_UpperCamelCase    , 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
			__lowerCAmelCase				    =			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.
						__lowerCAmelCase				    =			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":
									__lowerCAmelCase				    =			16
						elif accelerator.mixed_precision != "no":
									__lowerCAmelCase				    =			8
						else:
									__lowerCAmelCase				    =			None

						return tokenizer.pad(
						    _UpperCamelCase    , padding="longest"    , max_length=_UpperCamelCase    , pad_to_multiple_of=_UpperCamelCase    , return_tensors="pt"    , )

			# Instantiate dataloaders.
			__lowerCAmelCase				    =			DataLoader(
			    tokenized_datasets["train"]    , shuffle=_UpperCamelCase    , collate_fn=_UpperCamelCase    , batch_size=_UpperCamelCase    , drop_last=_UpperCamelCase      )
			__lowerCAmelCase				    =			DataLoader(
			    tokenized_datasets["validation"]    , shuffle=_UpperCamelCase    , collate_fn=_UpperCamelCase    , batch_size=_UpperCamelCase    , drop_last=(accelerator.mixed_precision == "fp8")    , )

			return train_dataloader, eval_dataloader


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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

			__lowerCAmelCase				    =			evaluate.load("glue"    , "mrpc"      )

			# If the batch size is too big we use gradient accumulation
			__lowerCAmelCase				    =			1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						__lowerCAmelCase				    =			batch_size // MAX_GPU_BATCH_SIZE
						__lowerCAmelCase				    =			MAX_GPU_BATCH_SIZE

			set_seed(_UpperCamelCase      )
			__lowerCAmelCase    ,	__lowerCAmelCase				    =			get_dataloaders(_UpperCamelCase    , _UpperCamelCase      )
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__lowerCAmelCase				    =			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"    , return_dict=_UpperCamelCase      )

			# 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).
			__lowerCAmelCase				    =			model.to(accelerator.device      )
			# Instantiate optimizer
			__lowerCAmelCase				    =			AdamW(params=model.parameters()    , lr=_UpperCamelCase      )

			# Instantiate scheduler
			__lowerCAmelCase				    =			get_linear_schedule_with_warmup(
			    optimizer=_UpperCamelCase    , num_warmup_steps=100    , num_training_steps=(len(_UpperCamelCase      ) * num_epochs) // gradient_accumulation_steps    , )

			# 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.

			__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase    ,	__lowerCAmelCase				    =			accelerator.prepare(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			# Now we train the model
			for epoch in range(_UpperCamelCase      ):
						model.train()
						for step, batch in enumerate(_UpperCamelCase      ):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device      )
									__lowerCAmelCase				    =			model(**_UpperCamelCase      )
									__lowerCAmelCase				    =			outputs.loss
									__lowerCAmelCase				    =			loss / gradient_accumulation_steps
									accelerator.backward(_UpperCamelCase      )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						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():
												__lowerCAmelCase				    =			model(**_UpperCamelCase      )
									__lowerCAmelCase				    =			outputs.logits.argmax(dim=-1      )
									__lowerCAmelCase    ,	__lowerCAmelCase				    =			accelerator.gather_for_metrics((predictions, batch["labels"])      )
									metric.add_batch(
									    predictions=_UpperCamelCase    , references=_UpperCamelCase    , )

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


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			argparse.ArgumentParser(description="Simple example of training script."      )
			parser.add_argument(
			    "--mixed_precision"    , type=_UpperCamelCase    , default=_UpperCamelCase    , 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."    , )
			parser.add_argument("--cpu"    , action="store_true"    , help="If passed, will train on the CPU."      )
			__lowerCAmelCase				    =			parser.parse_args()
			__lowerCAmelCase				    =			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
			training_function(_UpperCamelCase    , _UpperCamelCase      )


if __name__ == "__main__":
	main()








'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





