




'''simple docstring'''



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


UpperCAmelCase_       :  Any      =		1_6
UpperCAmelCase_       :  List[str]      =		3_2







def 			_UpperCamelCase (_lowerCamelCase							:     Union[str, Any]	)->   int:




     '''simple docstring'''





     return int(x / 2**20	)





class     lowerCAmelCase   :


       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
            __snake_case    					=  torch.cuda.memory_allocated()
            __snake_case    					=  psutil.Process()

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


       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''simple docstring'''




            return self.process.memory_info().rss


       def       lowerCAmelCase      (   self  )      ->  Any:



            '''simple docstring'''




            __snake_case    					=  -1

            while True:
                 __snake_case    					=  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    ,  *__SCREAMING_SNAKE_CASE  )      ->  Tuple:



            '''simple docstring'''




            __snake_case    					=  False

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

            __snake_case    					=  self.cpu_mem_used()
            __snake_case    					=  bamb(self.cpu_end - self.cpu_begin  )
            __snake_case    					=  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

     UpperCAmelCase_       :  str      =		mocked_dataloaders  # noqa: F811







def 			_UpperCamelCase (_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Optional[int]	)->   Any:




     '''simple docstring'''





     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,	_lowerCamelCase	) == "1":
          __snake_case    					=  2

     # New Code #
     # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
     __snake_case    					=  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:
          __snake_case    					=  Accelerator(
              cpu=args.cpu   ,	mixed_precision=args.mixed_precision   ,	log_with='''wandb'''   ,	project_dir=args.logging_dir   ,	fsdp_plugin=_lowerCamelCase   ,	)
     else:
          __snake_case    					=  Accelerator(fsdp_plugin=_lowerCamelCase	)
     accelerator.print(accelerator.distributed_type	)

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

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

     __snake_case    					=  AutoTokenizer.from_pretrained(args.model_name_or_path	)
     __snake_case    					=  load_dataset('''glue'''   ,	'''mrpc'''	)
     __snake_case    					=  evaluate.load('''glue'''   ,	'''mrpc'''	)

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

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

     def collate_fn(_lowerCamelCase							:     Optional[int]	):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __snake_case    					=  1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __snake_case    					=  16
          elif accelerator.mixed_precision != "no":
               __snake_case    					=  8
          else:
               __snake_case    					=  None

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

     # Instantiate dataloaders.
     __snake_case    					=  DataLoader(
         tokenized_datasets['''train''']   ,	shuffle=_lowerCamelCase   ,	collate_fn=_lowerCamelCase   ,	batch_size=_lowerCamelCase	)
     __snake_case    					=  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)
     __snake_case    					=  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
     __snake_case    					=  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
     __snake_case    					=  torch.optim.AdamW(params=model.parameters()   ,	lr=_lowerCamelCase   ,	weight_decay=2E-4	)

     # Instantiate scheduler
     __snake_case    					=  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.
     __snake_case					,    __snake_case					,    __snake_case					,    __snake_case    					=  accelerator.prepare(
         _lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase	)

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

          if "epoch" in training_difference:
               num_epochs -= int(training_difference.replace('''epoch_'''   ,	''''''	)	)
               __snake_case    					=  None
          else:
               __snake_case    					=  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
               __snake_case    					=  (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:
                    __snake_case    					=  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	)
                    __snake_case    					=  model(**_lowerCamelCase	)
                    __snake_case    					=  outputs.loss
                    __snake_case    					=  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	):
                         __snake_case    					=  f'''step_{overall_step}'''
                         if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                   __snake_case    					=  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():
                         __snake_case    					=  model(**_lowerCamelCase	)
                    __snake_case    					=  outputs.logits.argmax(dim=-1	)
                    __snake_case					,    __snake_case    					=  accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                    metric.add_batch(
                        predictions=_lowerCamelCase   ,	references=_lowerCamelCase   ,	)

               __snake_case    					=  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":
                    __snake_case    					=  f'''epoch_{epoch}'''
                    if args.output_dir is not None:
                         __snake_case    					=  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 ()->   Optional[Any]:




     '''simple docstring'''





     __snake_case    					=  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   ,	)
     __snake_case    					=  parser.parse_args()
     __snake_case    					=  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_lowerCamelCase   ,	_lowerCamelCase	)


if __name__ == "__main__":
     main()












'''simple docstring'''



import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


UpperCAmelCase_       :  str      =		datasets.utils.logging.get_logger(__name__)





@dataclass
class     lowerCAmelCase   ( datasets.BuilderConfig):
       __lowercase  :      Optional[datasets.Features]        =			None
       __lowercase  :      str        =			"utf-8"
       __lowercase  :      Optional[str]        =			None
       __lowercase  :      int        =			10 << 20  # 10MB
       __lowercase  :      bool        =			False
       __lowercase  :      str        =			"line"





class     lowerCAmelCase   ( datasets.ArrowBasedBuilder):
       __lowercase  :      Dict        =			TextConfig


       def       lowerCAmelCase      (   self  )      ->  Dict:



            '''simple docstring'''




            return datasets.DatasetInfo(features=self.config.features  )


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''simple docstring'''




            if not self.config.data_files:
                 raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''  )
            __snake_case    					=  dl_manager.download_and_extract(self.config.data_files  )
            if isinstance(__SCREAMING_SNAKE_CASE    ,  (str, list, tuple)  ):
                 __snake_case    					=  data_files
                 if isinstance(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  ):
                      __snake_case    					=  [files]
                 __snake_case    					=  [dl_manager.iter_files(__SCREAMING_SNAKE_CASE  ) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN    ,  gen_kwargs={'''files''': files}  )]
            __snake_case    					=  []
            for split_name, files in data_files.items():
                 if isinstance(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  ):
                      __snake_case    					=  [files]
                 __snake_case    					=  [dl_manager.iter_files(__SCREAMING_SNAKE_CASE  ) for file in files]
                 splits.append(datasets.SplitGenerator(name=__SCREAMING_SNAKE_CASE    ,  gen_kwargs={'''files''': files}  )  )
            return splits


       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  pa.Table:



            '''simple docstring'''




            if self.config.features is not None:
                 __snake_case    					=  self.config.features.arrow_schema
                 if all(not require_storage_cast(__SCREAMING_SNAKE_CASE  ) for feature in self.config.features.values()  ):
                      # cheaper cast
                      __snake_case    					=  pa_table.cast(__SCREAMING_SNAKE_CASE  )
                 else:
                      # more expensive cast; allows str <-> int/float or str to Audio for example
                      __snake_case    					=  table_cast(__SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )
                 return pa_table
            else:
                 return pa_table.cast(pa.schema({'''text''': pa.string()}  )  )




       def       lowerCAmelCase      (   self    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[Any]:



            '''simple docstring'''




            __snake_case    					=  list(self.config.features  ) if self.config.features is not None else ['''text''']
            for file_idx, file in enumerate(itertools.chain.from_iterable(__SCREAMING_SNAKE_CASE  )  ):
                 # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
                 with open(__SCREAMING_SNAKE_CASE    ,  encoding=self.config.encoding    ,  errors=self.config.errors  ) as f:
                      if self.config.sample_by == "line":
                           __snake_case    					=  0
                           while True:
                                __snake_case    					=  f.read(self.config.chunksize  )
                                if not batch:
                                     break
                                batch += f.readline()  # finish current line
                                # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
                                __snake_case    					=  StringIO(__SCREAMING_SNAKE_CASE  ).readlines()
                                if not self.config.keep_linebreaks:
                                     __snake_case    					=  [line.rstrip('''\n'''  ) for line in batch]
                                __snake_case    					=  pa.Table.from_arrays([pa.array(__SCREAMING_SNAKE_CASE  )]    ,  names=__SCREAMING_SNAKE_CASE  )
                                # Uncomment for debugging (will print the Arrow table size and elements)
                                # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                yield (file_idx, batch_idx), self._cast_table(__SCREAMING_SNAKE_CASE  )
                                batch_idx += 1
                      elif self.config.sample_by == "paragraph":
                           __snake_case    					=  0
                           __snake_case    					=  ''''''
                           while True:
                                __snake_case    					=  f.read(self.config.chunksize  )
                                if not new_batch:
                                     break
                                batch += new_batch
                                batch += f.readline()  # finish current line
                                __snake_case    					=  batch.split('''\n\n'''  )
                                __snake_case    					=  pa.Table.from_arrays(
                                    [pa.array([example for example in batch[:-1] if example]  )]    ,  names=__SCREAMING_SNAKE_CASE  )
                                # Uncomment for debugging (will print the Arrow table size and elements)
                                # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                                # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
                                yield (file_idx, batch_idx), self._cast_table(__SCREAMING_SNAKE_CASE  )
                                batch_idx += 1
                                __snake_case    					=  batch[-1]
                           if batch:
                                __snake_case    					=  pa.Table.from_arrays([pa.array([batch]  )]    ,  names=__SCREAMING_SNAKE_CASE  )
                                yield (file_idx, batch_idx), self._cast_table(__SCREAMING_SNAKE_CASE  )
                      elif self.config.sample_by == "document":
                           __snake_case    					=  f.read()
                           __snake_case    					=  pa.Table.from_arrays([pa.array([text]  )]    ,  names=__SCREAMING_SNAKE_CASE  )
                           yield file_idx, self._cast_table(__SCREAMING_SNAKE_CASE  )






