

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

a__				=		16
a__				=		32



def    __UpperCAmelCase (	__a      :			Union[str, Any]		,__a      :			Dict = 16	)			->     Optional[int]:

					"""simple docstring"""

					_a	:    List[str]         =		AutoTokenizer.from_pretrained('''bert-base-cased'''	)
					_a	:    str         =		load_dataset('''glue'''		,'''mrpc'''	)

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

					def collate_fn(__a      :			Any	):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										_a	:    str         =		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":
															_a	:    Optional[int]         =		16
										elif accelerator.mixed_precision != "no":
															_a	:    Union[str, Any]         =		8
										else:
															_a	:    Optional[Any]         =		None

										return tokenizer.pad(
										    UpperCamelCase_		,padding='''longest'''		,max_length=UpperCamelCase_		,pad_to_multiple_of=UpperCamelCase_		,return_tensors='''pt'''		,)

					# Instantiate dataloaders.
					_a	:    str         =		DataLoader(
					    tokenized_datasets['''train''']		,shuffle=UpperCamelCase_		,collate_fn=UpperCamelCase_		,batch_size=UpperCamelCase_	)
					_a	:    Tuple         =		DataLoader(
					    tokenized_datasets['''validation''']		,shuffle=UpperCamelCase_		,collate_fn=UpperCamelCase_		,batch_size=UpperCamelCase_	)

					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

			a__				=		mocked_dataloaders  # noqa: F811



def    __UpperCAmelCase (	__a      :			Optional[int]		,__a      :			int	)			->     Dict:

					"""simple docstring"""

					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,UpperCamelCase_	) == "1":
										_a	:    List[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:
										_a	:    List[str]         =		Accelerator(
										    cpu=args.cpu		,mixed_precision=args.mixed_precision		,log_with='''all'''		,project_dir=args.project_dir	)
					else:
										_a	:    List[Any]         =		Accelerator(cpu=args.cpu		,mixed_precision=args.mixed_precision	)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					_a	:    List[str]         =		config['''lr''']
					_a	:    Union[str, Any]         =		int(config['''num_epochs''']	)
					_a	:    Optional[Any]         =		int(config['''seed''']	)
					_a	:    Optional[int]         =		int(config['''batch_size''']	)
					set_seed(UpperCamelCase_	)

					_a      ,					_a	:    int         =		get_dataloaders(UpperCamelCase_		,UpperCamelCase_	)
					_a	:    Tuple         =		evaluate.load('''glue'''		,'''mrpc'''	)

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

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					_a	:    List[str]         =		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).
					_a	:    Any         =		model.to(accelerator.device	)

					# Instantiate optimizer
					_a	:    str         =		AdamW(params=model.parameters()		,lr=UpperCamelCase_	)

					# Instantiate scheduler
					_a	:    Dict         =		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.
					_a      ,					_a      ,					_a      ,					_a      ,					_a	:    Union[str, Any]         =		accelerator.prepare(
					    UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_		,UpperCamelCase_	)

					# New Code #
					# We need to initialize the trackers we use. Overall configurations can also be stored
					if args.with_tracking:
										_a	:    List[str]         =		os.path.split(UpperCamelCase_	)[-1].split('''.'''	)[0]
										accelerator.init_trackers(UpperCamelCase_		,UpperCamelCase_	)

					# Now we train the model
					for epoch in range(UpperCamelCase_	):
										model.train()
										# New Code #
										# For our tracking example, we will log the total loss of each epoch
										if args.with_tracking:
															_a	:    Any         =		0
										for step, batch in enumerate(UpperCamelCase_	):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device	)
															_a	:    Tuple         =		model(**UpperCamelCase_	)
															_a	:    Any         =		outputs.loss
															# New Code #
															if args.with_tracking:
																				total_loss += loss.detach().float()
															_a	:    Dict         =		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` (the default).
															batch.to(accelerator.device	)
															with torch.no_grad():
																				_a	:    Optional[Any]         =		model(**UpperCamelCase_	)
															_a	:    Optional[int]         =		outputs.logits.argmax(dim=-1	)
															_a      ,					_a	:    Any         =		accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
															metric.add_batch(
															    predictions=UpperCamelCase_		,references=UpperCamelCase_		,)

										_a	:    str         =		metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}:"""		,UpperCamelCase_	)

										# 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(UpperCamelCase_	),
															        '''epoch''': epoch,
															    }		,step=UpperCamelCase_		,)

    # 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 (	)			->     List[Any]:

					"""simple docstring"""

					_a	:    Any         =		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.'''	)
					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=UpperCamelCase_		,default='''logs'''		,help='''Location on where to store experiment tracking logs` and relevent project information'''		,)
					_a	:    Optional[Any]         =		parser.parse_args()
					_a	:    List[str]         =		{'''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, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  List[Any]						   =		'''BridgeTowerImageProcessor'''
		__lowercase							:  Tuple						   =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')




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




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

				return encoding




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




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




