
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   :     Dict      =						16
__lowerCAmelCase   :     int      =						32


def       UpperCAmelCase_					(  __lowerCAmelCase		)	->  Any:
    return int(x / 2**20		)


class   __lowerCAmelCase		:



      """simple docstring"""







      def __enter__( self						:    Optional[int]			):
          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          __lowercase						:     Union[str, Any]		      =    torch.cuda.memory_allocated()
          __lowercase						:     Dict		      =    psutil.Process()

          __lowercase						:     Tuple		      =    self.cpu_mem_used()
          __lowercase						:     Union[str, Any]		      =    True
          __lowercase						:     Union[str, Any]		      =    threading.Thread(target=self.peak_monitor_func			)
          __lowercase						:     List[str]		      =    True
          peak_monitor_thread.start()
          return self




      def 						snake_case_     ( self						:    Union[str, Any]			):
          return self.process.memory_info().rss




      def 						snake_case_     ( self						:    Optional[Any]			):
          __lowercase						:     List[Any]		      =    -1

          while True:
              __lowercase						:     Dict		      =    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						:    Tuple			,    *_snake_case						:    int			):
          __lowercase						:     Dict		      =    False

          gc.collect()
          torch.cuda.empty_cache()
          __lowercase						:     Tuple		      =    torch.cuda.memory_allocated()
          __lowercase						:     List[str]		      =    torch.cuda.max_memory_allocated()
          __lowercase						:     List[str]		      =    bamb(self.end - self.begin			)
          __lowercase						:     Optional[Any]		      =    bamb(self.peak - self.begin			)

          __lowercase						:     Union[str, Any]		      =    self.cpu_mem_used()
          __lowercase						:     Dict		      =    bamb(self.cpu_end - self.cpu_begin			)
          __lowercase						:     int		      =    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   :     Union[str, Any]      =						mocked_dataloaders  # noqa: F811


def       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  Any:

    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,   __lowerCAmelCase		) == "1":
        __lowercase						:     Union[str, Any]		      =    2

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

    # Initialize accelerator
    if args.with_tracking:
        __lowercase						:     Tuple		      =    Accelerator(
            cpu=args.cpu		,   mixed_precision=args.mixed_precision		,   log_with='''wandb'''		,   project_dir=args.logging_dir		,   fsdp_plugin=__lowerCAmelCase		,   )
    else:
        __lowercase						:     Optional[Any]		      =    Accelerator(fsdp_plugin=__lowerCAmelCase		)
    accelerator.print(accelerator.distributed_type		)

    if hasattr(args.checkpointing_steps		,   '''isdigit'''		):
        if args.checkpointing_steps == "epoch":
            __lowercase						:     Optional[Any]		      =    args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            __lowercase						:     Union[str, Any]		      =    int(args.checkpointing_steps		)
        else:
            raise ValueError(
                F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'		)
    else:
        __lowercase						:     str		      =    None
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    __lowercase						:     int		      =    config['''lr''']
    __lowercase						:     List[Any]		      =    int(config['''num_epochs''']		)
    __lowercase						:     str		      =    int(config['''seed''']		)
    __lowercase						:     Dict		      =    int(config['''batch_size''']		)

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        __lowercase						:     Any		      =    vars(__lowerCAmelCase		)
        accelerator.init_trackers('''fsdp_glue_no_trainer'''		,   __lowerCAmelCase		)

    __lowercase						:     Optional[int]		      =    AutoTokenizer.from_pretrained(args.model_name_or_path		)
    __lowercase						:     str		      =    load_dataset('''glue'''		,   '''mrpc'''		)
    __lowercase						:     List[str]		      =    evaluate.load('''glue'''		,   '''mrpc'''		)

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

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

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

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

    # Instantiate dataloaders.
    __lowercase						:     List[Any]		      =    DataLoader(
        tokenized_datasets['''train''']		,   shuffle=__lowerCAmelCase		,   collate_fn=__lowerCAmelCase		,   batch_size=__lowerCAmelCase		)
    __lowercase						:     Tuple		      =    DataLoader(
        tokenized_datasets['''validation''']		,   shuffle=__lowerCAmelCase		,   collate_fn=__lowerCAmelCase		,   batch_size=__lowerCAmelCase		)

    set_seed(__lowerCAmelCase		)

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

    # New Code #
    # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
    __lowercase						:     List[str]		      =    accelerator.prepare(__lowerCAmelCase		)
    accelerator.print(__lowerCAmelCase		)

    # 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
    __lowercase						:     str		      =    torch.optim.AdamW(params=model.parameters()		,   lr=__lowerCAmelCase		,   weight_decay=2E-4		)

    # Instantiate scheduler
    __lowercase						:     List[Any]		      =    get_linear_schedule_with_warmup(
        optimizer=__lowerCAmelCase		,   num_warmup_steps=10		,   num_training_steps=(len(__lowerCAmelCase		) * 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.
    __lowercase      , __lowercase      , __lowercase      , __lowercase						:     List[Any]		      =    accelerator.prepare(
        __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)

    __lowercase						:     Tuple		      =    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		)
            __lowercase						:     Any		      =    os.path.basename(args.resume_from_checkpoint		)
        else:
            # Get the most recent checkpoint
            __lowercase						:     Union[str, Any]		      =    [f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
            dirs.sort(key=os.path.getctime		)
            __lowercase						:     int		      =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        __lowercase						:     Dict		      =    os.path.splitext(__lowerCAmelCase		)[0]

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

    # Now we train the model
    for epoch in range(__lowerCAmelCase		):
        # New Code #
        # context manager to track the peak memory usage during the training epoch
        with TorchTracemalloc() as tracemalloc:
            model.train()
            if args.with_tracking:
                __lowercase						:     Optional[int]		      =    0
            for step, batch in enumerate(__lowerCAmelCase		):
                # 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		)
                __lowercase						:     List[Any]		      =    model(**__lowerCAmelCase		)
                __lowercase						:     Optional[Any]		      =    outputs.loss
                __lowercase						:     Union[str, Any]		      =    loss / gradient_accumulation_steps
                # We keep track of the loss at each epoch
                if args.with_tracking:
                    total_loss += loss.detach().float()
                accelerator.backward(__lowerCAmelCase		)
                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(__lowerCAmelCase		,   __lowerCAmelCase		):
                    __lowercase						:     Union[str, Any]		      =    F'step_{overall_step}'
                    if overall_step % checkpointing_steps == 0:
                        if args.output_dir is not None:
                            __lowercase						:     Any		      =    os.path.join(args.output_dir		,   __lowerCAmelCase		)
                        accelerator.save_state(__lowerCAmelCase		)
        # 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=__lowerCAmelCase		,   )

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

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

            if checkpointing_steps == "epoch":
                __lowercase						:     List[str]		      =    F'epoch_{epoch}'
                if args.output_dir is not None:
                    __lowercase						:     Optional[Any]		      =    os.path.join(args.output_dir		,   __lowerCAmelCase		)
                accelerator.save_state(__lowerCAmelCase		)
        # 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=__lowerCAmelCase		,   )

    if args.with_tracking:
        accelerator.end_training()


def       UpperCAmelCase_					(  )	->  str:
    __lowercase						:     str		      =    argparse.ArgumentParser(description='''Simple example of training script.'''		)
    parser.add_argument(
        '''--mixed_precision'''		,   type=__lowerCAmelCase		,   default=__lowerCAmelCase		,   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=__lowerCAmelCase		,   default=__lowerCAmelCase		,   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=__lowerCAmelCase		,   default=__lowerCAmelCase		,   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=__lowerCAmelCase		,   default='''.'''		,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''		,   )
    parser.add_argument(
        '''--logging_dir'''		,   type=__lowerCAmelCase		,   default='''logs'''		,   help='''Location on where to store experiment tracking logs`'''		,   )
    parser.add_argument(
        '''--model_name_or_path'''		,   type=__lowerCAmelCase		,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''		,   required=__lowerCAmelCase		,   )
    __lowercase						:     Optional[int]		      =    parser.parse_args()
    __lowercase						:     Any		      =    {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__lowerCAmelCase		,   __lowerCAmelCase		)


if __name__ == "__main__":
 main()




from ..utils import DummyObject, requires_backends


class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[Any]       				=      ['''tokenizers''']




      def __init__( self						:    Any			,    *_snake_case						:    Optional[int]			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    List[Any]			,    **_snake_case						:    Tuple			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      ['''tokenizers''']




      def __init__( self						:    List[Any]			,    *_snake_case						:    List[Any]			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[int]			,    *_snake_case						:    List[Any]			,    **_snake_case						:    int			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Optional[int]			,    *_snake_case						:    List[str]			,    **_snake_case						:    int			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[int]			,    *_snake_case						:    List[str]			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Any       				=      ['''tokenizers''']




      def __init__( self						:    List[str]			,    *_snake_case						:    Union[str, Any]			,    **_snake_case						:    Optional[int]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Any       				=      ['''tokenizers''']




      def __init__( self						:    Optional[Any]			,    *_snake_case						:    Tuple			,    **_snake_case						:    List[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     str       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    List[str]			,    **_snake_case						:    Dict			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[Any]       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    Optional[int]			,    **_snake_case						:    int			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[Any]       				=      ['''tokenizers''']




      def __init__( self						:    Any			,    *_snake_case						:    Optional[int]			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     str       				=      ['''tokenizers''']




      def __init__( self						:    Optional[Any]			,    *_snake_case						:    Tuple			,    **_snake_case						:    str			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     int       				=      ['''tokenizers''']




      def __init__( self						:    Optional[Any]			,    *_snake_case						:    List[Any]			,    **_snake_case						:    str			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Any       				=      ['''tokenizers''']




      def __init__( self						:    int			,    *_snake_case						:    Dict			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[Any]       				=      ['''tokenizers''']




      def __init__( self						:    int			,    *_snake_case						:    Optional[Any]			,    **_snake_case						:    List[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[Any]			,    *_snake_case						:    Dict			,    **_snake_case						:    Union[str, Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[Any]       				=      ['''tokenizers''']




      def __init__( self						:    List[str]			,    *_snake_case						:    Union[str, Any]			,    **_snake_case						:    List[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[Any]       				=      ['''tokenizers''']




      def __init__( self						:    List[str]			,    *_snake_case						:    int			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    List[Any]			,    *_snake_case						:    str			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      ['''tokenizers''']




      def __init__( self						:    Any			,    *_snake_case						:    List[Any]			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     int       				=      ['''tokenizers''']




      def __init__( self						:    List[str]			,    *_snake_case						:    Dict			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[Any]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[Any]			,    *_snake_case						:    Union[str, Any]			,    **_snake_case						:    Dict			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      ['''tokenizers''']




      def __init__( self						:    List[Any]			,    *_snake_case						:    List[str]			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Any       				=      ['''tokenizers''']




      def __init__( self						:    Union[str, Any]			,    *_snake_case						:    int			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      ['''tokenizers''']




      def __init__( self						:    Union[str, Any]			,    *_snake_case						:    str			,    **_snake_case						:    Union[str, Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Tuple       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    Any			,    **_snake_case						:    Tuple			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    int			,    *_snake_case						:    List[str]			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      ['''tokenizers''']




      def __init__( self						:    List[str]			,    *_snake_case						:    Optional[Any]			,    **_snake_case						:    Tuple			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    List[str]			,    **_snake_case						:    Union[str, Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    Union[str, Any]			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Any			,    *_snake_case						:    Optional[int]			,    **_snake_case						:    str			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[Any]       				=      ['''tokenizers''']




      def __init__( self						:    str			,    *_snake_case						:    List[str]			,    **_snake_case						:    Union[str, Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    Dict			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     str       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    Dict			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Any       				=      ['''tokenizers''']




      def __init__( self						:    Optional[int]			,    *_snake_case						:    List[Any]			,    **_snake_case						:    Tuple			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    str			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[Any]			,    *_snake_case						:    Dict			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     int       				=      ['''tokenizers''']




      def __init__( self						:    Any			,    *_snake_case						:    List[str]			,    **_snake_case						:    str			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    Dict			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Union[str, Any]			,    *_snake_case						:    Any			,    **_snake_case						:    Optional[int]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[int]			,    *_snake_case						:    List[Any]			,    **_snake_case						:    Any			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    str			,    **_snake_case						:    int			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     str       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    Tuple			,    **_snake_case						:    Dict			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[Any]       				=      ['''tokenizers''']




      def __init__( self						:    List[str]			,    *_snake_case						:    str			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[int]			,    *_snake_case						:    List[Any]			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    str			,    *_snake_case						:    Tuple			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     int       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    Any			,    **_snake_case						:    List[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     int       				=      ['''tokenizers''']




      def __init__( self						:    Any			,    *_snake_case						:    Optional[Any]			,    **_snake_case						:    List[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    str			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Any			,    *_snake_case						:    str			,    **_snake_case						:    Tuple			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    List[Any]			,    **_snake_case						:    Optional[int]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[int]       				=      ['''tokenizers''']




      def __init__( self						:    Optional[Any]			,    *_snake_case						:    Dict			,    **_snake_case						:    Optional[int]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    Dict			,    **_snake_case						:    int			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     str       				=      ['''tokenizers''']




      def __init__( self						:    Optional[int]			,    *_snake_case						:    List[str]			,    **_snake_case						:    str			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     List[str]       				=      ['''tokenizers''']




      def __init__( self						:    int			,    *_snake_case						:    str			,    **_snake_case						:    Union[str, Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Tuple			,    *_snake_case						:    Optional[Any]			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Dict       				=      ['''tokenizers''']




      def __init__( self						:    Dict			,    *_snake_case						:    Tuple			,    **_snake_case						:    Optional[Any]			):
          requires_backends(self			,    ['''tokenizers''']			)






class   __lowerCAmelCase		(		metaclass=lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Optional[Any]       				=      ['''tokenizers''']




      def __init__( self						:    List[str]			,    *_snake_case						:    Optional[int]			,    **_snake_case						:    List[str]			):
          requires_backends(self			,    ['''tokenizers''']			)


