


'''simple docstring'''







import argparse
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

_SCREAMING_SNAKE_CASE							: Dict										=			16
_SCREAMING_SNAKE_CASE							: Optional[int]										=			32



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__ = 16		):






				"""simple docstring"""



				__magic_name__					:       Tuple    							=							AutoTokenizer.from_pretrained("bert-base-cased"		)
				__magic_name__					:       List[Any]    							=							load_dataset("glue"  , "mrpc"		)

				def tokenize_function(UpperCamelCase__		):
								# max_length=None => use the model max length (it's actually the default)
								__magic_name__					:       Optional[Any]    							=							tokenizer(examples["sentence1"]  , examples["sentence2"]  , truncation=SCREAMING_SNAKE_CASE_  , max_length=SCREAMING_SNAKE_CASE_		)
								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():
								__magic_name__					:       List[str]    							=							datasets.map(
								    SCREAMING_SNAKE_CASE_  , batched=SCREAMING_SNAKE_CASE_  , 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
				__magic_name__					:       int    							=							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.
								__magic_name__					:       int    							=							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":
												__magic_name__					:       Dict    							=							16
								elif accelerator.mixed_precision != "no":
												__magic_name__					:       Dict    							=							8
								else:
												__magic_name__					:       List[str]    							=							None

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

				# Instantiate dataloaders.
				__magic_name__					:       Optional[int]    							=							DataLoader(
				    tokenized_datasets["train"]  , shuffle=SCREAMING_SNAKE_CASE_  , collate_fn=SCREAMING_SNAKE_CASE_  , batch_size=SCREAMING_SNAKE_CASE_		)
				__magic_name__					:       str    							=							DataLoader(
				    tokenized_datasets["validation"]  , shuffle=SCREAMING_SNAKE_CASE_  , collate_fn=SCREAMING_SNAKE_CASE_  , batch_size=SCREAMING_SNAKE_CASE_		)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		_SCREAMING_SNAKE_CASE							: Dict										=			mocked_dataloaders  # noqa: F811



def     _UpperCamelCase				(			UpperCamelCase__  , UpperCamelCase__		):






				"""simple docstring"""



				if os.environ.get("TESTING_MOCKED_DATALOADERS"  , SCREAMING_SNAKE_CASE_		) == "1":
								__magic_name__					:       Union[str, Any]    							=							2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__magic_name__					:       Tuple    							=							Accelerator(
								    cpu=args.cpu  , mixed_precision=args.mixed_precision  , log_with="all"  , project_dir=args.project_dir		)
				else:
								__magic_name__					:       Optional[Any]    							=							Accelerator(cpu=args.cpu  , mixed_precision=args.mixed_precision		)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__magic_name__					:       Optional[Any]    							=							config["lr"]
				__magic_name__					:       List[str]    							=							int(config["num_epochs"]		)
				__magic_name__					:       int    							=							int(config["seed"]		)
				__magic_name__					:       str    							=							int(config["batch_size"]		)
				set_seed(SCREAMING_SNAKE_CASE_		)

				__magic_name__							,  __magic_name__					:       List[str]    							=							get_dataloaders(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_		)
				__magic_name__					:       Tuple    							=							evaluate.load("glue"  , "mrpc"		)

				# If the batch size is too big we use gradient accumulation
				__magic_name__					:       Any    							=							1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__magic_name__					:       List[Any]    							=							batch_size // MAX_GPU_BATCH_SIZE
								__magic_name__					:       List[str]    							=							MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__magic_name__					:       List[Any]    							=							AutoModelForSequenceClassification.from_pretrained("bert-base-cased"  , return_dict=SCREAMING_SNAKE_CASE_		)

				# 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).
				__magic_name__					:       Any    							=							model.to(accelerator.device		)

				# Instantiate optimizer
				__magic_name__					:       Optional[int]    							=							AdamW(params=model.parameters()  , lr=SCREAMING_SNAKE_CASE_		)

				# Instantiate scheduler
				__magic_name__					:       List[Any]    							=							get_linear_schedule_with_warmup(
				    optimizer=SCREAMING_SNAKE_CASE_  , num_warmup_steps=100  , num_training_steps=(len(SCREAMING_SNAKE_CASE_		) * 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.
				__magic_name__							,  __magic_name__							,  __magic_name__							,  __magic_name__							,  __magic_name__					:       List[Any]    							=							accelerator.prepare(
				    SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_		)

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								__magic_name__					:       Optional[Any]    							=							os.path.split(SCREAMING_SNAKE_CASE_		)[-1].split("."		)[0]
								accelerator.init_trackers(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_		)

				# Now we train the model
				for epoch in range(SCREAMING_SNAKE_CASE_		):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												__magic_name__					:       List[str]    							=							0
								for step, batch in enumerate(SCREAMING_SNAKE_CASE_		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												__magic_name__					:       List[str]    							=							model(**SCREAMING_SNAKE_CASE_		)
												__magic_name__					:       List[str]    							=							outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__magic_name__					:       Optional[int]    							=							loss / gradient_accumulation_steps
												accelerator.backward(SCREAMING_SNAKE_CASE_		)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(SCREAMING_SNAKE_CASE_		):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device		)
												with torch.no_grad():
																__magic_name__					:       Dict    							=							model(**SCREAMING_SNAKE_CASE_		)
												__magic_name__					:       int    							=							outputs.logits.argmax(dim=-1		)
												__magic_name__							,  __magic_name__					:       Union[str, Any]    							=							accelerator.gather_for_metrics((predictions, batch["labels"])		)
												metric.add_batch(
												    predictions=SCREAMING_SNAKE_CASE_  , references=SCREAMING_SNAKE_CASE_  , )

								__magic_name__					:       Optional[Any]    							=							metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"""epoch {epoch}:"""  , SCREAMING_SNAKE_CASE_		)

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(SCREAMING_SNAKE_CASE_		),
												        "epoch": epoch,
												    }  , step=SCREAMING_SNAKE_CASE_  , )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()



def     _UpperCamelCase				(			):






				"""simple docstring"""



				__magic_name__					:       List[str]    							=							argparse.ArgumentParser(description="Simple example of training script."		)
				parser.add_argument(
				    "--mixed_precision"  , type=SCREAMING_SNAKE_CASE_  , default=SCREAMING_SNAKE_CASE_  , 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."		)
				parser.add_argument(
				    "--with_tracking"  , action="store_true"  , help="Whether to load in all available experiment trackers from the environment and use them for logging."  , )
				parser.add_argument(
				    "--project_dir"  , type=SCREAMING_SNAKE_CASE_  , default="logs"  , help="Location on where to store experiment tracking logs` and relevent project information"  , )
				__magic_name__					:       Dict    							=							parser.parse_args()
				__magic_name__					:       Dict    							=							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(SCREAMING_SNAKE_CASE_  , SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
		main()






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)