


import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
				import bitsandbytes as bnb

from copy import deepcopy


_lowercase       :      int	=	logging.getLogger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     torch.nn.Module							,					UpperCamelCase__:     BnbQuantizationConfig							,					UpperCamelCase__:     Union[str, os.PathLike] = None							,					UpperCamelCase__:     Optional[Dict[str, Union[int, str, torch.device]]] = None							,					UpperCamelCase__:     Optional[List[str]] = None							,					UpperCamelCase__:     Optional[Dict[Union[int, str], Union[int, str]]] = None							,					UpperCamelCase__:     Optional[Union[str, os.PathLike]] = None							,					UpperCamelCase__:     bool = False							,					) ->   Tuple:





	"""simple docstring"""







	A								=				bnb_quantization_config.load_in_abit
	A								=				bnb_quantization_config.load_in_abit

	if load_in_abit and not is_abit_bnb_available():
		raise ImportError(
		    """You have a version of `bitsandbytes` that is not compatible with 8bit quantization,"""
		    """ make sure you have the latest version of `bitsandbytes` installed."""						)
	if load_in_abit and not is_abit_bnb_available():
		raise ValueError(
		    """You have a version of `bitsandbytes` that is not compatible with 4bit quantization,"""
		    """make sure you have the latest version of `bitsandbytes` installed."""						)

	A								=				[]
	# custom device map
	if isinstance(UpperCamelCase__							,					UpperCamelCase__						) and len(device_map.keys()						) > 1:
		A								=				[key for key, value in device_map.items() if value in ["""disk""", """cpu"""]]

	# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
	if bnb_quantization_config.skip_modules is None:
		A								=				get_keys_to_not_convert(UpperCamelCase__						)

	# add cpu modules to skip modules only for 4-bit modules
	if load_in_abit:
		bnb_quantization_config.skip_modules.extend(UpperCamelCase__						)
	A								=				bnb_quantization_config.skip_modules

	# We add the modules we want to keep in full precision
	if bnb_quantization_config.keep_in_fpaa_modules is None:
		A								=				[]
	A								=				bnb_quantization_config.keep_in_fpaa_modules
	modules_to_not_convert.extend(UpperCamelCase__						)

	# compatibility with peft
	A								=				load_in_abit
	A								=				load_in_abit

	A								=				get_parameter_device(UpperCamelCase__						)
	if model_device.type != "meta":
		# quantization of an already loaded model
		logger.warning(
		    """It is not recommended to quantize a loaded model. """
		    """The model should be instantiated under the `init_empty_weights` context manager."""						)
		A								=				replace_with_bnb_layers(UpperCamelCase__							,					UpperCamelCase__							,					modules_to_not_convert=UpperCamelCase__						)
		# convert param to the right dtype
		A								=				bnb_quantization_config.torch_dtype
		for name, param in model.state_dict().items():
			if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules						):
				param.to(torch.floataa						)
				if param.dtype != torch.floataa:
					A								=				name.replace(""".weight"""							,					""""""						).replace(""".bias"""							,					""""""						)
					A								=				getattr(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
					if param is not None:
						param.to(torch.floataa						)
			elif torch.is_floating_point(UpperCamelCase__						):
				param.to(UpperCamelCase__						)
		if model_device.type == "cuda":
			# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
			model.cuda(torch.cuda.current_device()						)
			torch.cuda.empty_cache()
		elif torch.cuda.is_available():
			model.to(torch.cuda.current_device()						)
		else:
			raise RuntimeError("""No GPU found. A GPU is needed for quantization."""						)
		logger.info(
		    f'The model device type is {model_device.type}. However, cuda is needed for quantization.'
		    """We move the model to cuda."""						)
		return model

	elif weights_location is None:
		raise RuntimeError(
		    f'`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '						)

	else:
		with init_empty_weights():
			A								=				replace_with_bnb_layers(
			    UpperCamelCase__							,					UpperCamelCase__							,					modules_to_not_convert=UpperCamelCase__						)

		A								=				get_quantized_model_device_map(
		    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					max_memory=UpperCamelCase__							,					no_split_module_classes=UpperCamelCase__							,					)
		if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
			A								=				True

		A								=				any(x in list(device_map.values()						) for x in ["""cpu""", """disk"""]						)

		load_checkpoint_in_model(
		    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					dtype=bnb_quantization_config.torch_dtype							,					offload_folder=UpperCamelCase__							,					offload_state_dict=UpperCamelCase__							,					keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules							,					offload_abit_bnb=load_in_abit and offload							,					)
		return dispatch_model(UpperCamelCase__							,					device_map=UpperCamelCase__							,					offload_dir=UpperCamelCase__						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     List[str]=None							,					UpperCamelCase__:     Optional[int]=None							,					UpperCamelCase__:     str=None						) ->   Union[str, Any]:





	"""simple docstring"""







	if device_map is None:
		if torch.cuda.is_available():
			A								=				{"""""": torch.cuda.current_device()}
		else:
			raise RuntimeError("""No GPU found. A GPU is needed for quantization."""						)
		logger.info("""The device_map was not initialized.""" """Setting device_map to `{'':torch.cuda.current_device()}`."""						)

	if isinstance(UpperCamelCase__							,					UpperCamelCase__						):
		if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
			raise ValueError(
			    """If passing a string for `device_map`, please choose 'auto', 'balanced', 'balanced_low_0' or """
			    """'sequential'."""						)

		A								=				{}
		special_dtypes.update(
		    {
		        name: bnb_quantization_config.torch_dtype
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.skip_modules						)
		    }						)
		special_dtypes.update(
		    {
		        name: torch.floataa
		        for name, _ in model.named_parameters()
		        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules						)
		    }						)

		A								=				{}
		A								=				special_dtypes
		A								=				no_split_module_classes
		A								=				bnb_quantization_config.target_dtype

		# get max_memory for each device.
		if device_map != "sequential":
			A								=				get_balanced_memory(
			    UpperCamelCase__							,					low_zero=(device_map == """balanced_low_0""")							,					max_memory=UpperCamelCase__							,					**UpperCamelCase__							,					)

		A								=				max_memory
		A								=				infer_auto_device_map(UpperCamelCase__							,					**UpperCamelCase__						)

	if isinstance(UpperCamelCase__							,					UpperCamelCase__						):
		# check if don't have any quantized module on the cpu
		A								=				bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

		A								=				{
		    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
		}
		for device in ["cpu", "disk"]:
			if device in device_map_without_some_modules.values():
				if bnb_quantization_config.load_in_abit:
					raise ValueError(
					    """
                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
                        these modules in `torch_dtype`, you need to pass a custom `device_map` to
                        `load_and_quantize_model`. Check
                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
                        for more details.
                        """						)
				else:
					logger.info(
					    """Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit"""						)
		del device_map_without_some_modules
	return device_map





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Dict=None							,					UpperCamelCase__:     List[str]=None						) ->   List[str]:





	"""simple docstring"""







	if modules_to_not_convert is None:
		A								=				[]

	A					,			A								=				_replace_with_bnb_layers(
	    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
	if not has_been_replaced:
		logger.warning(
		    """You are loading your model in 8bit or 4bit but no linear modules were found in your model."""
		    """ this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers."""
		    """ Please double check your model architecture, or submit an issue on github if you think this is"""
		    """ a bug."""						)
	return model





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int=None							,					UpperCamelCase__:     Optional[int]=None							,					) ->   Union[str, Any]:





	"""simple docstring"""







	A								=				False
	for name, module in model.named_children():
		if current_key_name is None:
			A								=				[]
		current_key_name.append(UpperCamelCase__						)
		if isinstance(UpperCamelCase__							,					nn.Linear						) and name not in modules_to_not_convert:
			# Check if the current key is not in the `modules_to_not_convert`
			A								=				""".""".join(UpperCamelCase__						)
			A								=				True
			for key in modules_to_not_convert:
				if (
				    (key in current_key_name_str) and (key + "." in current_key_name_str)
				) or key == current_key_name_str:
					A								=				False
					break
			if proceed:
				# Load bnb module with empty weight and replace ``nn.Linear` module
				if bnb_quantization_config.load_in_abit:
					A								=				bnb.nn.LinearabitLt(
					    module.in_features							,					module.out_features							,					module.bias is not None							,					has_fpaa_weights=UpperCamelCase__							,					threshold=bnb_quantization_config.llm_inta_threshold							,					)
				elif bnb_quantization_config.load_in_abit:
					A								=				bnb.nn.Linearabit(
					    module.in_features							,					module.out_features							,					module.bias is not None							,					bnb_quantization_config.bnb_abit_compute_dtype							,					compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant							,					quant_type=bnb_quantization_config.bnb_abit_quant_type							,					)
				else:
					raise ValueError("""load_in_8bit and load_in_4bit can't be both False"""						)
				A								=				module.weight.data
				if module.bias is not None:
					A								=				module.bias.data
				bnb_module.requires_grad_(UpperCamelCase__						)
				setattr(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
				A								=				True
		if len(list(module.children()						)						) > 0:
			A					,			A								=				_replace_with_bnb_layers(
			    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)
			A								=				has_been_replaced | _has_been_replaced
		# Remove the last key for recursion
		current_key_name.pop(-1						)
	return model, has_been_replaced





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   List[Any]:





	"""simple docstring"""







	with init_empty_weights():
		A								=				deepcopy(UpperCamelCase__						)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

	A								=				find_tied_parameters(UpperCamelCase__						)
	# For compatibility with Accelerate < 0.18
	if isinstance(UpperCamelCase__							,					UpperCamelCase__						):
		A								=				sum(list(tied_params.values()						)							,					[]						) + list(tied_params.keys()						)
	else:
		A								=				sum(UpperCamelCase__							,					[]						)
	A								=				len(UpperCamelCase__						) > 0

	# Check if it is a base model
	A								=				False
	if hasattr(UpperCamelCase__							,					"""base_model_prefix"""						):
		A								=				not hasattr(UpperCamelCase__							,					model.base_model_prefix						)

	# Ignore this for base models (BertModel, GPT2Model, etc.)
	if (not has_tied_params) and is_base_model:
		return []

	# otherwise they have an attached head
	A								=				list(model.named_children()						)
	A								=				[list_modules[-1][0]]

	# add last module together with tied weights
	A								=				set(UpperCamelCase__						) - set(UpperCamelCase__						)
	A								=				list(set(UpperCamelCase__						)						) + list(UpperCamelCase__						)

	# remove ".weight" from the keys
	A								=				[""".weight""", """.bias"""]
	A								=				[]
	for name in list_untouched:
		for name_to_remove in names_to_remove:
			if name_to_remove in name:
				A								=				name.replace(UpperCamelCase__							,					""""""						)
		filtered_module_names.append(UpperCamelCase__						)

	return filtered_module_names





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict						) ->   Any:





	"""simple docstring"""







	for m in model.modules():
		if isinstance(UpperCamelCase__							,					bnb.nn.Linearabit						):
			return True
	return False





def   _lowerCAmelCase				(   UpperCamelCase__:     nn.Module						) ->   Any:





	"""simple docstring"""







	return next(parameter.parameters()						).device





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     int							,					UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   Optional[int]:





	"""simple docstring"""







	if fpaa_statistics is None:
		set_module_tensor_to_device(UpperCamelCase__							,					UpperCamelCase__							,					0							,					dtype=UpperCamelCase__							,					value=UpperCamelCase__						)
		A								=				param_name
		A								=				model
		if "." in tensor_name:
			A								=				tensor_name.split("""."""						)
			for split in splits[:-1]:
				A								=				getattr(UpperCamelCase__							,					UpperCamelCase__						)
				if new_module is None:
					raise ValueError(f'{module} has no attribute {split}.'						)
				A								=				new_module
			A								=				splits[-1]
		# offload weights
		A								=				False
		offload_weight(module._parameters[tensor_name]							,					UpperCamelCase__							,					UpperCamelCase__							,					index=UpperCamelCase__						)
		if hasattr(module._parameters[tensor_name]							,					"""SCB"""						):
			offload_weight(
			    module._parameters[tensor_name].SCB							,					param_name.replace("""weight"""							,					"""SCB"""						)							,					UpperCamelCase__							,					index=UpperCamelCase__							,					)
	else:
		offload_weight(UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					index=UpperCamelCase__						)
		offload_weight(UpperCamelCase__							,					param_name.replace("""weight"""							,					"""SCB"""						)							,					UpperCamelCase__							,					index=UpperCamelCase__						)

	set_module_tensor_to_device(UpperCamelCase__							,					UpperCamelCase__							,					"""meta"""							,					dtype=UpperCamelCase__							,					value=torch.empty(*param.size()						)						)







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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_lowercase       :      Optional[Any]	=	16
_lowercase       :      List[Any]	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     int = 16						) ->   Tuple:





	"""simple docstring"""







	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				load_dataset("""glue"""							,					"""mrpc"""						)

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

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		A								=				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								=				tokenized_datasets.rename_column("""label"""							,					"""labels"""						)

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

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

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

	return train_dataloader, eval_dataloader


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

				_lowercase       :      List[Any]	=	mocked_dataloaders  # noqa: F811





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,					UpperCamelCase__						) == "1":
		A								=				2
	# Initialize accelerator
	A								=				Accelerator(cpu=args.cpu							,					mixed_precision=args.mixed_precision						)
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A								=				config["""lr"""]
	A								=				int(config["""num_epochs"""]						)
	A								=				int(config["""seed"""]						)
	A								=				int(config["""batch_size"""]						)

	A								=				evaluate.load("""glue"""							,					"""mrpc"""						)

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

	set_seed(UpperCamelCase__						)
	A					,			A								=				get_dataloaders(UpperCamelCase__							,					UpperCamelCase__						)
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A								=				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								=				model.to(accelerator.device						)

	# Instantiate optimizer
	A								=				AdamW(params=model.parameters()							,					lr=UpperCamelCase__						)

	# Instantiate scheduler
	A								=				get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__							,					num_warmup_steps=1_00							,					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								=				accelerator.prepare(
	    UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__							,					UpperCamelCase__						)

	# Now we train the model
	for epoch in range(UpperCamelCase__						):
		model.train()
		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								=				model(**UpperCamelCase__						)
			A								=				outputs.loss
			A								=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase__						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		A								=				0
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				A								=				model(**UpperCamelCase__						)
			A								=				outputs.logits.argmax(dim=-1						)
			A					,			A								=				accelerator.gather((predictions, batch["""labels"""])						)
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(UpperCamelCase__						) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					A								=				predictions[: len(eval_dataloader.dataset						) - samples_seen]
					A								=				references[: len(eval_dataloader.dataset						) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=UpperCamelCase__							,					references=UpperCamelCase__							,					)

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





def   _lowerCAmelCase				(   ) ->   Dict:





	"""simple docstring"""







	A								=				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."""						)
	A								=				parser.parse_args()
	A								=				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				main()



