


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# 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)
#
# 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 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     # Initialize accelerator
     if args.with_tracking:
          a_     :  str				      =     Accelerator(
              cpu=args.cpu	,	mixed_precision=args.mixed_precision	,	log_with="""all"""	,	project_dir=args.project_dir			)
     else:
          a_     :  Any				      =     Accelerator(cpu=args.cpu	,	mixed_precision=args.mixed_precision			)

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

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          a_     :  Union[str, Any]				      =     os.path.split(__UpperCamelCase			)[-1].split("""."""			)[0]
          accelerator.init_trackers(__UpperCamelCase	,	__UpperCamelCase			)

     a_     :  int				      =     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
     a_     :  Tuple				      =     load_dataset("""glue"""	,	"""mrpc"""			)
     a_     :  Optional[int]				      =     evaluate.load("""glue"""	,	"""mrpc"""			)

     def tokenize_function(__UpperCamelCase			):
          # max_length=None => use the model max length (it's actually the default)
          a_     :  List[Any]				      =     tokenizer(examples["""sentence1"""]	,	examples["""sentence2"""]	,	truncation=__UpperCamelCase	,	max_length=__UpperCamelCase			)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          a_     :  int				      =     datasets.map(
              __UpperCamelCase	,	batched=__UpperCamelCase	,	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_     :  Tuple				      =     tokenized_datasets.rename_column("""label"""	,	"""labels"""			)

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

     def collate_fn(__UpperCamelCase			):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          a_     :  str				      =     1_2_8 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_     :  Dict				      =     1_6
          elif accelerator.mixed_precision != "no":
               a_     :  Optional[Any]				      =     8
          else:
               a_     :  Dict				      =     None

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

     # Instantiate dataloaders.
     a_     :  List[str]				      =     DataLoader(
         tokenized_datasets["""train"""]	,	shuffle=__UpperCamelCase	,	collate_fn=__UpperCamelCase	,	batch_size=__UpperCamelCase			)
     a_     :  Optional[Any]				      =     DataLoader(
         tokenized_datasets["""validation"""]	,	shuffle=__UpperCamelCase	,	collate_fn=__UpperCamelCase	,	batch_size=__UpperCamelCase			)

     set_seed(__UpperCamelCase			)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     a_     :  List[Any]				      =     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""	,	return_dict=__UpperCamelCase			)

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     a_     :  Any				      =     model.to(accelerator.device			)

     # Instantiate optimizer
     a_     :  int				      =     AdamW(params=model.parameters()	,	lr=__UpperCamelCase			)

     # Instantiate scheduler
     a_     :  List[Any]				      =     get_linear_schedule_with_warmup(
         optimizer=__UpperCamelCase	,	num_warmup_steps=1_0_0	,	num_training_steps=(len(__UpperCamelCase			) * num_epochs) // gradient_accumulation_steps	,	)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     a_      ,      a_      ,      a_      ,      a_      ,      a_     :  List[str]				      =     accelerator.prepare(
         __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)

     # We need to keep track of how many total steps we have iterated over
     a_     :  Optional[Any]				      =     0
     # We also need to keep track of the stating epoch so files are named properly
     a_     :  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			)
               a_     :  List[str]				      =     os.path.basename(args.resume_from_checkpoint			)
          else:
               # Get the most recent checkpoint
               a_     :  Optional[Any]				      =     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
               dirs.sort(key=os.path.getctime			)
               a_     :  str				      =     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          a_     :  Optional[int]				      =     os.path.splitext(__UpperCamelCase			)[0]

          if "epoch" in training_difference:
               a_     :  Tuple				      =     int(training_difference.replace("""epoch_"""	,	""""""			)			) + 1
               a_     :  List[str]				      =     None
          else:
               a_     :  Any				      =     int(training_difference.replace("""step_"""	,	""""""			)			)
               a_     :  Dict				      =     resume_step // len(__UpperCamelCase			)
               resume_step -= starting_epoch * len(__UpperCamelCase			)

    # Now we train the model
     for epoch in range(__UpperCamelCase	,	__UpperCamelCase			):
          model.train()
          if args.with_tracking:
               a_     :  Optional[int]				      =     0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               a_     :  Dict				      =     accelerator.skip_first_batches(__UpperCamelCase	,	__UpperCamelCase			)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               a_     :  Optional[int]				      =     train_dataloader
          for step, batch in enumerate(__UpperCamelCase			):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device			)
               a_     :  Optional[Any]				      =     model(**__UpperCamelCase			)
               a_     :  List[str]				      =     outputs.loss
               a_     :  List[str]				      =     loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(__UpperCamelCase			)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(__UpperCamelCase	,	__UpperCamelCase			):
                    a_     :  Optional[int]				      =     F'''step_{overall_step}'''
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              a_     :  Tuple				      =     os.path.join(args.output_dir	,	__UpperCamelCase			)
                         accelerator.save_state(__UpperCamelCase			)

          model.eval()
          for step, batch in enumerate(__UpperCamelCase			):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device			)
               with torch.no_grad():
                    a_     :  Optional[Any]				      =     model(**__UpperCamelCase			)
               a_     :  Optional[Any]				      =     outputs.logits.argmax(dim=-1			)
               a_      ,      a_     :  List[Any]				      =     accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
               metric.add_batch(
                   predictions=__UpperCamelCase	,	references=__UpperCamelCase	,	)

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

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

     if args.with_tracking:
          accelerator.end_training()



def 			_a      (    ):
     a_     :  Any				      =     argparse.ArgumentParser(description="""Simple example of training script."""			)
     parser.add_argument(
         """--mixed_precision"""	,	type=__UpperCamelCase	,	default=__UpperCamelCase	,	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=__UpperCamelCase	,	default=__UpperCamelCase	,	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=__UpperCamelCase	,	default=__UpperCamelCase	,	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=__UpperCamelCase	,	default="""."""	,	help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""	,	)
     parser.add_argument(
         """--project_dir"""	,	type=__UpperCamelCase	,	default="""logs"""	,	help="""Location on where to store experiment tracking logs` and relevent project information"""	,	)
     a_     :  List[Any]				      =     parser.parse_args()
     a_     :  Dict				      =     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
     training_function(__UpperCamelCase	,	__UpperCamelCase			)


if __name__ == "__main__":
       main()











'''simple docstring'''







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


UpperCamelCase_					=				os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
UpperCamelCase_					=				os.path.join(hf_cache_home, "accelerate")
UpperCamelCase_					=				os.path.join(cache_dir, "default_config.yaml")
UpperCamelCase_					=				os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
  UpperCamelCase_					=				default_yaml_config_file
else:
  UpperCamelCase_					=				default_json_config_file






def  lowerCAmelCase__		(					a_  :     List[Any]			)   -> Optional[Any]:
       if config_file is not None:
              if not os.path.isfile(a_			):
                     raise FileNotFoundError(
                         f"""The passed configuration file `{config_file}` does not exist. """
                         '''Please pass an existing file to `accelerate launch`, or use the the default one '''
                         '''created through `accelerate config` and run `accelerate launch` '''
                         '''without the `--config_file` argument.'''			)
       else:
              UpperCAmelCase__	:   Dict												=	default_config_file
       with open(a_			,						'''r'''			,						encoding='''utf-8'''			) as f:
              if config_file.endswith('''.json'''			):
                     if (
                         json.load(a_			).get('''compute_environment'''			,						ComputeEnvironment.LOCAL_MACHINE			)
                         == ComputeEnvironment.LOCAL_MACHINE
                     ):
                            UpperCAmelCase__	:   Optional[Any]												=	ClusterConfig
                     else:
                            UpperCAmelCase__	:   Dict												=	SageMakerConfig
                     return config_class.from_json_file(json_file=a_			)
              else:
                     if (
                         yaml.safe_load(a_			).get('''compute_environment'''			,						ComputeEnvironment.LOCAL_MACHINE			)
                         == ComputeEnvironment.LOCAL_MACHINE
                     ):
                            UpperCAmelCase__	:   Dict												=	ClusterConfig
                     else:
                            UpperCAmelCase__	:   int												=	SageMakerConfig
                     return config_class.from_yaml_file(yaml_file=a_			)




@dataclass
class 					__UpperCAmelCase :

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    ComputeEnvironment
      SCREAMING_SNAKE_CASE				:    Union[DistributedType, SageMakerDistributedType]
      SCREAMING_SNAKE_CASE				:    str
      SCREAMING_SNAKE_CASE				:    bool




      def 							lowerCamelCase     (					self						):
             UpperCAmelCase__	:   List[Any]												=	self.__dict__
             # For serialization, it's best to convert Enums to strings (or their underlying value type).
             for key, value in result.items():
                    if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						):
                           UpperCAmelCase__	:   Union[str, Any]												=	value.value
                    if isinstance(_UpperCAmelCase  ,		_UpperCAmelCase						) and not bool(_UpperCAmelCase						):
                           UpperCAmelCase__	:   Optional[int]												=	None
             UpperCAmelCase__	:   Optional[Any]												=	{k: v for k, v in result.items() if v is not None}
             return result




      @classmethod
      def 							lowerCamelCase     (					cls  ,		_UpperCAmelCase=None						):
             UpperCAmelCase__	:   str												=	default_json_config_file if json_file is None else json_file
             with open(_UpperCAmelCase  ,		'''r'''  ,		encoding='''utf-8'''						) as f:
                    UpperCAmelCase__	:   Optional[int]												=	json.load(_UpperCAmelCase						)
             if "compute_environment" not in config_dict:
                    UpperCAmelCase__	:   List[str]												=	ComputeEnvironment.LOCAL_MACHINE
             if "mixed_precision" not in config_dict:
                    UpperCAmelCase__	:   Dict												=	'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
             if "fp16" in config_dict:  # Convert the config to the new format.
                    del config_dict["fp16"]
             if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                    UpperCAmelCase__	:   int												=	config_dict.pop('''dynamo_backend'''						)
                    UpperCAmelCase__	:   int												=	{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
             if "use_cpu" not in config_dict:
                    UpperCAmelCase__	:   Optional[Any]												=	False
             return cls(**_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             with open(_UpperCAmelCase  ,		'''w'''  ,		encoding='''utf-8'''						) as f:
                    UpperCAmelCase__	:   Any												=	json.dumps(self.to_dict()  ,		indent=2  ,		sort_keys=_UpperCAmelCase						) + '''\n'''
                    f.write(_UpperCAmelCase						)




      @classmethod
      def 							lowerCamelCase     (					cls  ,		_UpperCAmelCase=None						):
             UpperCAmelCase__	:   List[Any]												=	default_yaml_config_file if yaml_file is None else yaml_file
             with open(_UpperCAmelCase  ,		'''r'''  ,		encoding='''utf-8'''						) as f:
                    UpperCAmelCase__	:   str												=	yaml.safe_load(_UpperCAmelCase						)
             if "compute_environment" not in config_dict:
                    UpperCAmelCase__	:   List[Any]												=	ComputeEnvironment.LOCAL_MACHINE

             if "mixed_precision" not in config_dict:
                    UpperCAmelCase__	:   Tuple												=	'''fp16''' if ('''fp16''' in config_dict and config_dict['''fp16''']) else None
             if isinstance(config_dict['''mixed_precision''']  ,		_UpperCAmelCase						) and not config_dict["mixed_precision"]:
                    UpperCAmelCase__	:   Any												=	'''no'''
             if "fp16" in config_dict:  # Convert the config to the new format.
                    del config_dict["fp16"]
             if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                    UpperCAmelCase__	:   Optional[int]												=	config_dict.pop('''dynamo_backend'''						)
                    UpperCAmelCase__	:   Dict												=	{} if dynamo_backend == '''NO''' else {'''dynamo_backend''': dynamo_backend}
             if "use_cpu" not in config_dict:
                    UpperCAmelCase__	:   List[str]												=	False
             return cls(**_UpperCAmelCase						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase						):
             with open(_UpperCAmelCase  ,		'''w'''  ,		encoding='''utf-8'''						) as f:
                    yaml.safe_dump(self.to_dict()  ,		_UpperCAmelCase						)




      def 							lowerCamelCase     (					self						):
             if isinstance(self.compute_environment  ,		_UpperCAmelCase						):
                    UpperCAmelCase__	:   Tuple												=	ComputeEnvironment(self.compute_environment						)
             if isinstance(self.distributed_type  ,		_UpperCAmelCase						):
                    if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                           UpperCAmelCase__	:   Optional[Any]												=	SageMakerDistributedType(self.distributed_type						)
                    else:
                           UpperCAmelCase__	:   Dict												=	DistributedType(self.distributed_type						)
             if self.dynamo_config is None:
                    UpperCAmelCase__	:   Optional[Any]												=	{}




@dataclass
class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    int
      SCREAMING_SNAKE_CASE				:    int				       =					0
      SCREAMING_SNAKE_CASE				:    int				       =					1
      SCREAMING_SNAKE_CASE				:    Optional[str]				       =					None
      SCREAMING_SNAKE_CASE				:    Optional[str]				       =					None
      SCREAMING_SNAKE_CASE				:    Optional[int]				       =					None
      SCREAMING_SNAKE_CASE				:    Optional[str]				       =					"static"
      SCREAMING_SNAKE_CASE				:    Optional[bool]				       =					False
      SCREAMING_SNAKE_CASE				:    str				       =					"main"

      # args for deepspeed_plugin
      SCREAMING_SNAKE_CASE				:    dict				       =					None
      # args for fsdp
      SCREAMING_SNAKE_CASE				:    dict				       =					None
      # args for megatron_lm
      SCREAMING_SNAKE_CASE				:    dict				       =					None
      # args for ipex
      SCREAMING_SNAKE_CASE				:    dict				       =					None
      # args for TPU
      SCREAMING_SNAKE_CASE				:    bool				       =					False

      # args for TPU pods
      SCREAMING_SNAKE_CASE				:    str				       =					None
      SCREAMING_SNAKE_CASE				:    str				       =					None
      SCREAMING_SNAKE_CASE				:    bool				       =					False
      SCREAMING_SNAKE_CASE				:    bool				       =					False
      SCREAMING_SNAKE_CASE				:    str				       =					None
      SCREAMING_SNAKE_CASE				:    List[str]				       =					None
      SCREAMING_SNAKE_CASE				:    List[str]				       =					None
      SCREAMING_SNAKE_CASE				:    List[str]				       =					None

      # args for dynamo
      SCREAMING_SNAKE_CASE				:    dict				       =					None




      def 							lowerCamelCase     (					self						):
             if self.deepspeed_config is None:
                    UpperCAmelCase__	:   Union[str, Any]												=	{}
             if self.fsdp_config is None:
                    UpperCAmelCase__	:   Any												=	{}
             if self.megatron_lm_config is None:
                    UpperCAmelCase__	:   Tuple												=	{}
             if self.ipex_config is None:
                    UpperCAmelCase__	:   List[str]												=	{}
             return super().__post_init__()




@dataclass
class 					__UpperCAmelCase (		UpperCamelCase__	):

      '''simple docstring'''





      SCREAMING_SNAKE_CASE				:    str
      SCREAMING_SNAKE_CASE				:    str
      SCREAMING_SNAKE_CASE				:    Optional[str]				       =					None
      SCREAMING_SNAKE_CASE				:    Optional[str]				       =					None
      SCREAMING_SNAKE_CASE				:    str				       =					"us-east-1"
      SCREAMING_SNAKE_CASE				:    int				       =					1
      SCREAMING_SNAKE_CASE				:    str				       =					"all"
      SCREAMING_SNAKE_CASE				:    str				       =					F"accelerate-sagemaker-{num_machines}"
      SCREAMING_SNAKE_CASE				:    str				       =					SAGEMAKER_PYTORCH_VERSION
      SCREAMING_SNAKE_CASE				:    str				       =					SAGEMAKER_TRANSFORMERS_VERSION
      SCREAMING_SNAKE_CASE				:    str				       =					SAGEMAKER_PYTHON_VERSION
      SCREAMING_SNAKE_CASE				:    str				       =					None
      SCREAMING_SNAKE_CASE				:    str				       =					None
      SCREAMING_SNAKE_CASE				:    dict				       =					None
      SCREAMING_SNAKE_CASE				:    dict				       =					None