
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		: Dict								=     16
A		: Union[str, Any]								=     32




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Accelerator		,   SCREAMING_SNAKE_CASE_	:	int = 16		,   SCREAMING_SNAKE_CASE_	:	str = "bert-base-cased"       )  ->     Optional[int]:
						_lowercase					       =					AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					load_dataset("""glue"""		,   """mrpc"""       )

						def tokenize_function(SCREAMING_SNAKE_CASE_	:	Any       ):
												# max_length=None => use the model max length (it's actually the default)
												_lowercase					       =					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
						_lowercase					       =					datasets.map(
						    SCREAMING_SNAKE_CASE_		,   batched=SCREAMING_SNAKE_CASE_		,   remove_columns=["""idx""", """sentence1""", """sentence2"""]		,   load_from_cache_file=SCREAMING_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					       =					tokenized_datasets.rename_column("""label"""		,   """labels"""       )

						def collate_fn(SCREAMING_SNAKE_CASE_	:	Any       ):
												# 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(SCREAMING_SNAKE_CASE_		,   padding="""max_length"""		,   max_length=1_28		,   return_tensors="""pt"""       )
												return tokenizer.pad(SCREAMING_SNAKE_CASE_		,   padding="""longest"""		,   return_tensors="""pt"""       )

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

						return train_dataloader, eval_dataloader




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]		,   SCREAMING_SNAKE_CASE_	:	Any		,   SCREAMING_SNAKE_CASE_	:	Dict		,   SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     List[Any]:
						model.eval()
						_lowercase					       =					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       )
												with torch.no_grad():
																		_lowercase					       =					model(**SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					outputs.logits.argmax(dim=-1       )
												# It is slightly faster to call this once, than multiple times
												_lowercase       ,						_lowercase					       =					accelerator.gather(
												    (predictions, batch["""labels"""])       )  # If we are in a multiprocess environment, the last batch has duplicates
												if accelerator.use_distributed:
																		if step == len(SCREAMING_SNAKE_CASE_       ) - 1:
																								_lowercase					       =					predictions[: len(eval_dataloader.dataset       ) - samples_seen]
																								_lowercase					       =					references[: len(eval_dataloader.dataset       ) - samples_seen]
																		else:
																								samples_seen += references.shape[0]
												metric.add_batch(
												    predictions=SCREAMING_SNAKE_CASE_		,   references=SCREAMING_SNAKE_CASE_		,   )

						_lowercase					       =					metric.compute()
						return eval_metric["accuracy"]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Optional[Any]:
						# Initialize accelerator
						_lowercase					       =					Accelerator()

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

						set_seed(SCREAMING_SNAKE_CASE_       )
						_lowercase       ,						_lowercase					       =					get_dataloaders(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )

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

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

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

						# We need to keep track of how many total steps we have iterated over
						_lowercase					       =					0
						# We also need to keep track of the stating epoch so files are named properly
						_lowercase					       =					0
						_lowercase					       =					evaluate.load("""glue"""		,   """mrpc"""       )
						_lowercase					       =					num_epochs

						if args.partial_train_epoch is not None:
												_lowercase					       =					args.partial_train_epoch

						if args.resume_from_checkpoint:
												accelerator.load_state(args.resume_from_checkpoint       )
												_lowercase					       =					args.resume_from_checkpoint.split("""epoch_"""       )[1]
												_lowercase					       =					""""""
												for char in epoch_string:
																		if char.isdigit():
																								state_epoch_num += char
																		else:
																								break
												_lowercase					       =					int(SCREAMING_SNAKE_CASE_       ) + 1
												_lowercase					       =					evaluation_loop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												accelerator.print("""resumed checkpoint performance:"""		,   SCREAMING_SNAKE_CASE_       )
												accelerator.print("""resumed checkpoint's scheduler's lr:"""		,   lr_scheduler.get_lr()[0]       )
												accelerator.print("""resumed optimizers's lr:"""		,   optimizer.param_groups[0]["""lr"""]       )
												with open(os.path.join(args.output_dir		,   f"""state_{starting_epoch-1}.json"""       )		,   """r"""       ) as f:
																		_lowercase					       =					json.load(SCREAMING_SNAKE_CASE_       )
																		assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["lr"] == lr_scheduler.get_lr()[0]
																		), "Scheduler learning rate mismatch, loading from checkpoint failed"
																		assert (
																		    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
																		), "Optimizer learning rate mismatch, loading from checkpoint failed"
																		assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
																		return

    # Now we train the model
						_lowercase					       =					{}
						for epoch in range(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       ):
												model.train()
												for step, batch in enumerate(SCREAMING_SNAKE_CASE_       ):
																		_lowercase					       =					model(**SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					outputs.loss
																		_lowercase					       =					loss / gradient_accumulation_steps
																		accelerator.backward(SCREAMING_SNAKE_CASE_       )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1
												_lowercase					       =					f"""epoch_{epoch}"""
												_lowercase					       =					os.path.join(args.output_dir		,   SCREAMING_SNAKE_CASE_       )
												accelerator.save_state(SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					evaluation_loop(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					accuracy
												_lowercase					       =					lr_scheduler.get_lr()[0]
												_lowercase					       =					optimizer.param_groups[0]["""lr"""]
												_lowercase					       =					epoch
												_lowercase					       =					overall_step
												accelerator.print(f"""epoch {epoch}:"""		,   SCREAMING_SNAKE_CASE_       )

												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		with open(os.path.join(args.output_dir		,   f"""state_{epoch}.json"""       )		,   """w"""       ) as f:
																								json.dump(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     )  ->     Optional[int]:
						_lowercase					       =					argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""       )
						parser.add_argument(
						    """--model_name_or_path"""		,   type=SCREAMING_SNAKE_CASE_		,   default="""bert-base-cased"""		,   help="""Path to pretrained model or model identifier from huggingface.co/models."""		,   required=SCREAMING_SNAKE_CASE_		,   )
						parser.add_argument(
						    """--output_dir"""		,   type=SCREAMING_SNAKE_CASE_		,   default="""."""		,   help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,   )
						parser.add_argument(
						    """--resume_from_checkpoint"""		,   type=SCREAMING_SNAKE_CASE_		,   default=SCREAMING_SNAKE_CASE_		,   help="""If the training should continue from a checkpoint folder."""		,   )
						parser.add_argument(
						    """--partial_train_epoch"""		,   type=SCREAMING_SNAKE_CASE_		,   default=SCREAMING_SNAKE_CASE_		,   help="""If passed, the training will stop after this number of epochs."""		,   )
						parser.add_argument(
						    """--num_epochs"""		,   type=SCREAMING_SNAKE_CASE_		,   default=2		,   help="""Number of train epochs."""		,   )
						_lowercase					       =					parser.parse_args()
						_lowercase					       =					{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

						training_function(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )


if __name__ == "__main__":
		main()

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("""You need to specify an `image_processor`."""  )
									if tokenizer is None:
															raise ValueError("""You need to specify a `tokenizer`."""  )

									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			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  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class