


"""simple docstring"""


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase						:				Optional[Any]       =      logging.get_logger(__name__)

UpperCamelCase						:				Any       =      {
    "microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}



class 			lowerCamelCase__						(   _UpperCamelCase			):
						lowerCAmelCase       =     """git_vision_model"""







						def __init__(							self   :    Tuple			,			_lowercase   :    Optional[int]=768			,			_lowercase   :    Dict=3_072			,			_lowercase   :    List[Any]=12			,			_lowercase   :    Optional[Any]=12			,			_lowercase   :    Optional[int]=3			,			_lowercase   :    List[Any]=224			,			_lowercase   :    List[str]=16			,			_lowercase   :    List[str]="quick_gelu"			,			_lowercase   :    Dict=1e-5			,			_lowercase   :    int=0.0			,			_lowercase   :    List[Any]=0.0_2			,			**_lowercase   :    Any			,			):
								super().__init__(**_UpperCAmelCase		)

								A					       = hidden_size
								A					       = intermediate_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = num_channels
								A					       = patch_size
								A					       = image_size
								A					       = initializer_range
								A					       = attention_dropout
								A					       = layer_norm_eps
								A					       = hidden_act







						@classmethod
						def __a						(							cls   :    Union[str, Any]			,			_lowercase   :    Union[str, Any]			,			**_lowercase   :    List[str]		):
								cls._set_token_in_kwargs(_UpperCAmelCase		)

								A					       = cls.get_config_dict(_UpperCAmelCase			,			**_UpperCAmelCase		)

								# get the vision config dict if we are loading from GITConfig
								if config_dict.get('model_type'		) == "git":
										A					       = config_dict['''vision_config''']

								if "model_type" in config_dict and hasattr(cls			,			'model_type'		) and config_dict["model_type"] != cls.model_type:
										logger.warning(
										    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
										    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'		)

								return cls.from_dict(_UpperCAmelCase			,			**_UpperCAmelCase		)



class 			lowerCamelCase__						(   _UpperCamelCase			):
						lowerCAmelCase       =     """git"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Optional[Any]=None			,			_lowercase   :    Optional[Any]=30_522			,			_lowercase   :    int=768			,			_lowercase   :    Optional[Any]=6			,			_lowercase   :    int=12			,			_lowercase   :    List[str]=3_072			,			_lowercase   :    Optional[Any]="gelu"			,			_lowercase   :    List[str]=0.1			,			_lowercase   :    Any=0.1			,			_lowercase   :    List[Any]=1_024			,			_lowercase   :    Optional[Any]=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    int=0			,			_lowercase   :    Optional[Any]="absolute"			,			_lowercase   :    List[str]=True			,			_lowercase   :    int=False			,			_lowercase   :    Optional[int]=101			,			_lowercase   :    Any=102			,			_lowercase   :    Union[str, Any]=None			,			**_lowercase   :    List[Any]			,			):
								super().__init__(bos_token_id=_UpperCAmelCase			,			eos_token_id=_UpperCAmelCase			,			pad_token_id=_UpperCAmelCase			,			**_UpperCAmelCase		)

								if vision_config is None:
										A					       = {}
										logger.info('vision_config is None. initializing the GitVisionConfig with default values.'		)

								A					       = GitVisionConfig(**_UpperCAmelCase		)
								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = use_cache
								A					       = tie_word_embeddings
								A					       = num_image_with_embedding

								A					       = bos_token_id
								A					       = eos_token_id







						def __a						(							self   :    Any		):
								A					       = copy.deepcopy(self.__dict__		)
								A					       = self.vision_config.to_dict()
								A					       = self.__class__.model_type
								return output









"""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


UpperCamelCase						:				int             =      16
UpperCamelCase						:				Optional[int]             =      32







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ = 16 ,			UpperCamelCase__ = "bert-base-cased"	)      ->		Optional[int]:







		"""simple docstring"""





		A					       = AutoTokenizer.from_pretrained(UpperCamelCase__	)
		A					       = load_dataset('glue' ,			'mrpc'	)

		def tokenize_function(UpperCamelCase__	):
				# max_length=None => use the model max length (it's actually the default)
				A					       = 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
		A					       = datasets.map(
		    UpperCamelCase__ ,			batched=UpperCamelCase__ ,			remove_columns=['idx', 'sentence1', 'sentence2'] ,			load_from_cache_file=UpperCamelCase__	)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		A					       = 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.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(UpperCamelCase__ ,			padding='max_length' ,			max_length=128 ,			return_tensors='pt'	)
				return tokenizer.pad(UpperCamelCase__ ,			padding='longest' ,			return_tensors='pt'	)

		# Instantiate dataloaders.
		A					       = DataLoader(
		    tokenized_datasets['train'] ,			shuffle=UpperCamelCase__ ,			collate_fn=UpperCamelCase__ ,			batch_size=UpperCamelCase__	)
		A					       = DataLoader(
		    tokenized_datasets['validation'] ,			shuffle=UpperCamelCase__ ,			collate_fn=UpperCamelCase__ ,			batch_size=UpperCamelCase__	)

		return train_dataloader, eval_dataloader







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





		A					       = Accelerator()

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

		set_seed(UpperCamelCase__	)
		A       , A					       = get_dataloaders(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

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

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

		if accelerator.state.deepspeed_plugin is not None:
				A					       = accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				A					       = 1
		A					       = (len(UpperCamelCase__	) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				A					       = get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase__ ,			num_warmup_steps=0 ,			num_training_steps=UpperCamelCase__ ,			)
		else:
				A					       = DummyScheduler(UpperCamelCase__ ,			total_num_steps=UpperCamelCase__ ,			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.
		A       , A       , A       , A       , A					       = accelerator.prepare(
		    UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)

		# We need to keep track of how many total steps we have iterated over
		A					       = 0
		# We also need to keep track of the stating epoch so files are named properly
		A					       = 0

		# Now we train the model
		A					       = evaluate.load('glue' ,			'mrpc'	)
		A					       = 0
		A					       = {}
		for epoch in range(UpperCamelCase__ ,			UpperCamelCase__	):
				model.train()
				for step, batch in enumerate(UpperCamelCase__	):
						A					       = model(**UpperCamelCase__	)
						A					       = outputs.loss
						A					       = loss / gradient_accumulation_steps
						accelerator.backward(UpperCamelCase__	)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

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

				A					       = metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:' ,			UpperCamelCase__	)
				A					       = eval_metric['accuracy']

				if best_performance < eval_metric["accuracy"]:
						A					       = 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(UpperCamelCase__ ,			UpperCamelCase__	)







def  __snake_case       (   )      ->		Optional[int]:







		"""simple docstring"""





		A					       = argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'	)
		parser.add_argument(
		    '--model_name_or_path' ,			type=UpperCamelCase__ ,			default='bert-base-cased' ,			help='Path to pretrained model or model identifier from huggingface.co/models.' ,			required=UpperCamelCase__ ,			)
		parser.add_argument(
		    '--output_dir' ,			type=UpperCamelCase__ ,			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=UpperCamelCase__ ,			default=UpperCamelCase__ ,			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=UpperCamelCase__ ,			default=3 ,			help='Number of train epochs.' ,			)
		A					       = parser.parse_args()
		A					       = {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
		training_function(UpperCamelCase__ ,			UpperCamelCase__	)


if __name__ == "__main__":
	main()





