



import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


lowerCamelCase__     =	16
lowerCamelCase__     =	32

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Union[str, Any]      ):
    """simple docstring"""







    return int(x / 2**20      )






class 				SCREAMING_SNAKE_CASE     :





       def __enter__(				self						:						Optional[int]		):

           '''simple docstring'''
           gc.collect()
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
           __a					       =						torch.cuda.memory_allocated()
           __a					       =						psutil.Process()

           __a					       =						self.cpu_mem_used()
           __a					       =						True
           __a					       =						threading.Thread(target=self.peak_monitor_func		)
           __a					       =						True
           peak_monitor_thread.start()
           return self





       def   UpperCamelCase_   (				self						:						List[Any]		):

           '''simple docstring'''
           return self.process.memory_info().rss





       def   UpperCamelCase_   (				self						:						Dict		):

           '''simple docstring'''
           __a					       =						-1

           while True:
               __a					       =						max(self.cpu_mem_used() ,			self.cpu_peak		)

               # can't sleep or will not catch the peak right (this comment is here on purpose)
               # time.sleep(0.001) # 1msec

               if not self.peak_monitoring:
                   break







       def __exit__(				self						:						Optional[int] ,			*__lowercase						:						Any		):

           '''simple docstring'''
           __a					       =						False

           gc.collect()
           torch.cuda.empty_cache()
           __a					       =						torch.cuda.memory_allocated()
           __a					       =						torch.cuda.max_memory_allocated()
           __a					       =						bamb(self.end - self.begin		)
           __a					       =						bamb(self.peak - self.begin		)

           __a					       =						self.cpu_mem_used()
           __a					       =						bamb(self.cpu_end - self.cpu_begin		)
           __a					       =						bamb(self.cpu_peak - self.cpu_begin		)
           # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

      lowerCamelCase__     =	mocked_dataloaders  # noqa: F811

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     str      ):
    """simple docstring"""







    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,       lowercase_      ) == "1":
        __a					       =						2

    # New Code #
    # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
    __a					       =						FullyShardedDataParallelPlugin(
        state_dict_config=FullStateDictConfig(offload_to_cpu=lowercase_			,       ranka_only=lowercase_      )			,       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowercase_			,       ranka_only=lowercase_      )			,       )

    # Initialize accelerator
    if args.with_tracking:
        __a					       =						Accelerator(
            cpu=args.cpu			,       mixed_precision=args.mixed_precision			,       log_with="""wandb"""			,       project_dir=args.logging_dir			,       fsdp_plugin=lowercase_			,       )
    else:
        __a					       =						Accelerator(fsdp_plugin=lowercase_      )
    accelerator.print(accelerator.distributed_type      )

    if hasattr(args.checkpointing_steps			,       """isdigit"""      ):
        if args.checkpointing_steps == "epoch":
            __a					       =						args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __a					       =						int(args.checkpointing_steps      )
        else:
            raise ValueError(
                f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."      )
    else:
        __a					       =						None
    # 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"""]      )

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        __a					       =						vars(lowercase_      )
        accelerator.init_trackers("""fsdp_glue_no_trainer"""			,       lowercase_      )

    __a					       =						AutoTokenizer.from_pretrained(args.model_name_or_path      )
    __a					       =						load_dataset("""glue"""			,       """mrpc"""      )
    __a					       =						evaluate.load("""glue"""			,       """mrpc"""      )

    def tokenize_function(_SCREAMING_SNAKE_CASE    :     Optional[int]      ):
        # max_length=None => use the model max length (it's actually the default)
        __a					       =						tokenizer(examples["""sentence1"""]			,       examples["""sentence2"""]			,       truncation=lowercase_			,       max_length=lowercase_      )
        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					       =						datasets.map(
            lowercase_			,       batched=lowercase_			,       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					       =						tokenized_datasets.rename_column("""label"""			,       """labels"""      )

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

    def collate_fn(_SCREAMING_SNAKE_CASE    :     Dict      ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __a					       =						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					       =						16
        elif accelerator.mixed_precision != "no":
            __a					       =						8
        else:
            __a					       =						None

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

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

    set_seed(lowercase_      )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __a					       =						AutoModelForSequenceClassification.from_pretrained(
        args.model_name_or_path			,       return_dict=lowercase_			,       low_cpu_mem_usage=lowercase_      )

    # New Code #
    # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
    __a					       =						accelerator.prepare(lowercase_      )
    accelerator.print(lowercase_      )

    # Instantiate optimizer
    # New Code #
    # For FSDP feature, at present it doesn't support multiple parameter groups,
    # so we need to create a single parameter group for the whole model
    __a					       =						torch.optim.AdamW(params=model.parameters()			,       lr=lowercase_			,       weight_decay=2e-4      )

    # Instantiate scheduler
    __a					       =						get_linear_schedule_with_warmup(
        optimizer=lowercase_			,       num_warmup_steps=10			,       num_training_steps=(len(lowercase_      ) * num_epochs) // gradient_accumulation_steps			,       )

    # New Code #
    # For FSDP feature, prepare everything except the model as we have already prepared the model
    # before creating the optimizer
    # 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					       =						accelerator.prepare(
        lowercase_			,       lowercase_			,       lowercase_			,       lowercase_      )

    __a					       =						0

    # Potentially load in the weights and states from a previous save
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(f"Resumed from checkpoint: {args.resume_from_checkpoint}"      )
            accelerator.load_state(args.resume_from_checkpoint      )
            __a					       =						os.path.basename(args.resume_from_checkpoint      )
        else:
            # Get the most recent checkpoint
            __a					       =						[f.name for f in os.scandir(os.getcwd()      ) if f.is_dir()]
            dirs.sort(key=os.path.getctime      )
            __a					       =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __a					       =						os.path.splitext(lowercase_      )[0]

        if "epoch" in training_difference:
            num_epochs -= int(training_difference.replace("""epoch_"""			,       """"""      )      )
            __a					       =						None
        else:
            __a					       =						int(training_difference.replace("""step_"""			,       """"""      )      )
            num_epochs -= resume_step // len(lowercase_      )
            # If resuming by step, we also need to know exactly how far into the DataLoader we went
            __a					       =						(num_epochs * len(lowercase_      )) - resume_step

             # Now we train the model
    for epoch in range(lowercase_      ):
        # New Code #
        # context manager to track the peak memory usage during the training epoch
        with TorchTracemalloc() as tracemalloc:
            model.train()
            if args.with_tracking:
                __a					       =						0
            for step, batch in enumerate(lowercase_      ):
                # We need to skip steps until we reach the resumed step
                if args.resume_from_checkpoint and epoch == 0:
                    if resume_step is not None and step < resume_step:
                        pass
                                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device      )
                __a					       =						model(**lowercase_      )
                __a					       =						outputs.loss
                __a					       =						loss / gradient_accumulation_steps
                # We keep track of the loss at each epoch
                if args.with_tracking:
                    total_loss += loss.detach().float()
                accelerator.backward(lowercase_      )
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()
                    # accelerator.print(lr_scheduler.get_lr())

                overall_step += 1

                if isinstance(lowercase_			,       lowercase_      ):
                    __a					       =						f"step_{overall_step}"
                    if overall_step % checkpointing_steps == 0:
                        if args.output_dir is not None:
                            __a					       =						os.path.join(args.output_dir			,       lowercase_      )
                        accelerator.save_state(lowercase_      )
                          # New Code #
                          # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
        accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin      )      )      )
        accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used      )      )
        accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked      )      )
        accelerator.print(
            """Total Peak Memory consumed during the train (max): {}""".format(
                tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
        # Logging the peak memory usage of the GPU to the tracker
        if args.with_tracking:
            accelerator.log(
                {
                    """train_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin      ),
                }			,       step=lowercase_			,       )

        # New Code #
        # context manager to track the peak memory usage during the evaluation
        with TorchTracemalloc() as tracemalloc:
            model.eval()
            for step, batch in enumerate(lowercase_      ):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device      )
                with torch.no_grad():
                    __a					       =						model(**lowercase_      )
                __a					       =						outputs.logits.argmax(dim=-1      )
                __a       ,   __a					       =						accelerator.gather_for_metrics((predictions, batch["""labels"""])      )
                metric.add_batch(
                    predictions=lowercase_			,       references=lowercase_			,       )

            __a					       =						metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:"			,       lowercase_      )
            if args.with_tracking:
                accelerator.log(
                    {
                        """accuracy""": eval_metric["""accuracy"""],
                        """f1""": eval_metric["""f1"""],
                        """train_loss""": total_loss.item() / len(lowercase_      ),
                    }			,       step=lowercase_			,       )

            if checkpointing_steps == "epoch":
                __a					       =						f"epoch_{epoch}"
                if args.output_dir is not None:
                    __a					       =						os.path.join(args.output_dir			,       lowercase_      )
                accelerator.save_state(lowercase_      )
                    # New Code #
                    # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
        accelerator.print("""Memory before entering the eval : {}""".format(bamb(tracemalloc.begin      )      )      )
        accelerator.print("""Memory consumed at the end of the eval (end-begin): {}""".format(tracemalloc.used      )      )
        accelerator.print("""Peak Memory consumed during the eval (max-begin): {}""".format(tracemalloc.peaked      )      )
        accelerator.print(
            """Total Peak Memory consumed during the eval (max): {}""".format(tracemalloc.peaked + bamb(tracemalloc.begin      )      )      )
        # Logging the peak memory usage of the GPU to the tracker
        if args.with_tracking:
            accelerator.log(
                {
                    """eval_total_peak_memory""": tracemalloc.peaked + bamb(tracemalloc.begin      ),
                }			,       step=lowercase_			,       )

    if args.with_tracking:
        accelerator.end_training()

def 					lowerCAmelCase__  (							):
    """simple docstring"""







    __a					       =						argparse.ArgumentParser(description="""Simple example of training script."""      )
    parser.add_argument(
        """--mixed_precision"""			,       type=lowercase_			,       default=lowercase_			,       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(
        """--checkpointing_steps"""			,       type=lowercase_			,       default=lowercase_			,       help="""Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch."""			,       )
    parser.add_argument(
        """--resume_from_checkpoint"""			,       type=lowercase_			,       default=lowercase_			,       help="""If the training should continue from a checkpoint folder."""			,       )
    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(
        """--output_dir"""			,       type=lowercase_			,       default="""."""			,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""			,       )
    parser.add_argument(
        """--logging_dir"""			,       type=lowercase_			,       default="""logs"""			,       help="""Location on where to store experiment tracking logs`"""			,       )
    parser.add_argument(
        """--model_name_or_path"""			,       type=lowercase_			,       help="""Path to pretrained model or model identifier from huggingface.co/models."""			,       required=lowercase_			,       )
    __a					       =						parser.parse_args()
    __a					       =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(lowercase_			,       lowercase_      )


if __name__ == "__main__":
      main()





import copy

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


lowerCamelCase__     =	logging.get_logger(__name__)

lowerCamelCase__     =	{
    """facebook/sam-vit-huge""": """https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json""",
    """facebook/sam-vit-large""": """https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json""",
    """facebook/sam-vit-base""": """https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json""",
}






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						List[Any] ,			__lowercase						:						Optional[Any]=256 ,			__lowercase						:						Optional[int]=1024 ,			__lowercase						:						Any=16 ,			__lowercase						:						Optional[Any]=16 ,			__lowercase						:						Optional[Any]=4 ,			__lowercase						:						Optional[int]="gelu" ,			__lowercase						:						List[Any]=1E-6 ,			**__lowercase						:						str ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						hidden_size
											__a					       =						image_size
											__a					       =						patch_size
											__a					       =						image_size // patch_size
											__a					       =						mask_input_channels
											__a					       =						num_point_embeddings
											__a					       =						hidden_act
											__a					       =						layer_norm_eps






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						int ,			__lowercase						:						Tuple=256 ,			__lowercase						:						List[Any]="relu" ,			__lowercase						:						Any=2048 ,			__lowercase						:						List[Any]=2 ,			__lowercase						:						List[str]=8 ,			__lowercase						:						Any=2 ,			__lowercase						:						List[Any]=3 ,			__lowercase						:						str=3 ,			__lowercase						:						str=256 ,			__lowercase						:						Optional[Any]=1E-6 ,			**__lowercase						:						Tuple ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						hidden_size
											__a					       =						hidden_act
											__a					       =						mlp_dim
											__a					       =						num_hidden_layers
											__a					       =						num_attention_heads
											__a					       =						attention_downsample_rate
											__a					       =						num_multimask_outputs
											__a					       =						iou_head_depth
											__a					       =						iou_head_hidden_dim
											__a					       =						layer_norm_eps






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):





							def __init__(				self						:						List[Any] ,			__lowercase						:						List[Any]=768 ,			__lowercase						:						List[Any]=256 ,			__lowercase						:						Any=12 ,			__lowercase						:						Tuple=12 ,			__lowercase						:						Union[str, Any]=3 ,			__lowercase						:						Tuple=1024 ,			__lowercase						:						Any=16 ,			__lowercase						:						Dict="gelu" ,			__lowercase						:						List[Any]=1E-06 ,			__lowercase						:						List[str]=0.0 ,			__lowercase						:						Optional[Any]=1E-10 ,			__lowercase						:						Optional[Any]=True ,			__lowercase						:						str=4.0 ,			__lowercase						:						List[Any]=True ,			__lowercase						:						Dict=True ,			__lowercase						:						Optional[Any]=14 ,			__lowercase						:						Optional[int]=[2, 5, 8, 11] ,			__lowercase						:						List[str]=128 ,			__lowercase						:						Optional[int]=None ,			**__lowercase						:						List[Any] ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)

											__a					       =						hidden_size
											__a					       =						output_channels
											__a					       =						num_hidden_layers
											__a					       =						num_attention_heads
											__a					       =						num_channels
											__a					       =						image_size
											__a					       =						patch_size
											__a					       =						hidden_act
											__a					       =						layer_norm_eps
											__a					       =						attention_dropout
											__a					       =						initializer_range
											__a					       =						qkv_bias
											__a					       =						mlp_ratio
											__a					       =						use_abs_pos
											__a					       =						use_rel_pos
											__a					       =						window_size
											__a					       =						global_attn_indexes
											__a					       =						num_pos_feats
											__a					       =						int(hidden_size * mlp_ratio		) if mlp_dim is None else mlp_dim






class 				SCREAMING_SNAKE_CASE     (							lowerCamelCase__   ):
							__lowerCamelCase		:		Dict      ='sam'
							__lowerCamelCase		:		Any      =True





							def __init__(				self						:						List[str] ,			__lowercase						:						List[str]=None ,			__lowercase						:						Optional[int]=None ,			__lowercase						:						Dict=None ,			__lowercase						:						Optional[int]=0.02 ,			**__lowercase						:						List[str] ,			):

											'''simple docstring'''
											super().__init__(**__lowercase		)
											__a					       =						vision_config if vision_config is not None else {}
											__a					       =						prompt_encoder_config if prompt_encoder_config is not None else {}
											__a					       =						mask_decoder_config if mask_decoder_config is not None else {}

											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						vision_config.to_dict()
											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						prompt_encoder_config.to_dict()
											if isinstance(__lowercase ,			__lowercase		):
															__a					       =						mask_decoder_config.to_dict()

											__a					       =						SamVisionConfig(**__lowercase		)
											__a					       =						SamPromptEncoderConfig(**__lowercase		)
											__a					       =						SamMaskDecoderConfig(**__lowercase		)
											__a					       =						initializer_range







							def   UpperCamelCase_   (				self						:						Union[str, Any]		):

											'''simple docstring'''
											__a					       =						copy.deepcopy(self.__dict__		)
											__a					       =						self.vision_config.to_dict()
											__a					       =						self.prompt_encoder_config.to_dict()
											__a					       =						self.mask_decoder_config.to_dict()
											__a					       =						self.__class__.model_type
											return output
