



'''simple docstring'''

import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
	import bitsandbytes as bnb
	import torch
	import torch.nn as nn

	from ..pytorch_utils import ConvaD

if is_accelerate_available():
	from accelerate import init_empty_weights
	from accelerate.utils import find_tied_parameters

UpperCAmelCase     		=   logging.get_logger(__name__)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]=None							,		_SCREAMING_SNAKE_CASE					:						Any=None							)      ->						Tuple:







	"""simple docstring"""


	if "." in tensor_name:
		lowerCAmelCase   =     tensor_name.split("""."""							)
		for split in splits[:-1]:
			lowerCAmelCase   =     getattr(lowerCAmelCase__							,		lowerCAmelCase__							)
			if new_module is None:
				raise ValueError(f'{module} has no attribute {split}.'							)
			lowerCAmelCase   =     new_module
		lowerCAmelCase   =     splits[-1]

	if tensor_name not in module._parameters and tensor_name not in module._buffers:
		raise ValueError(f'{module} does not have a parameter or a buffer named {tensor_name}.'							)
	lowerCAmelCase   =     tensor_name in module._buffers
	lowerCAmelCase   =     getattr(lowerCAmelCase__							,		lowerCAmelCase__							)

	if old_value.device == torch.device("""meta"""							) and device not in ["meta", torch.device("""meta"""							)] and value is None:
		raise ValueError(f'{tensor_name} is on the meta device, we need a `value` to put in on {device}.'							)

	lowerCAmelCase   =     False
	lowerCAmelCase   =     False
	if is_buffer or not is_bitsandbytes_available():
		lowerCAmelCase   =     False
		lowerCAmelCase   =     False
	else:
		lowerCAmelCase   =     hasattr(bnb.nn							,		"""Params4bit"""							) and isinstance(module._parameters[tensor_name]							,		bnb.nn.Paramsabit							)
		lowerCAmelCase   =     isinstance(module._parameters[tensor_name]							,		bnb.nn.IntaParams							)

	if is_abit or is_abit:
		lowerCAmelCase   =     module._parameters[tensor_name]
		if param.device.type != "cuda":
			if value is None:
				lowerCAmelCase   =     old_value.to(lowerCAmelCase__							)
			elif isinstance(lowerCAmelCase__							,		torch.Tensor							):
				lowerCAmelCase   =     value.to("""cpu"""							)
				if value.dtype == torch.inta:
					lowerCAmelCase   =     version.parse(importlib.metadata.version("""bitsandbytes"""							)							) > version.parse(
					    """0.37.2"""							)
					if not is_abit_serializable:
						raise ValueError(
						    """Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. """
						    """Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."""							)
			else:
				lowerCAmelCase   =     torch.tensor(lowerCAmelCase__							,		device="""cpu"""							)

			# Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
			# Since weights are saved in the correct "orientation", we skip transposing when loading.
			if issubclass(module.source_cls							,		lowerCAmelCase__							) and fpaa_statistics is None:
				lowerCAmelCase   =     new_value.T

			lowerCAmelCase   =     old_value.__dict__
			if is_abit:
				lowerCAmelCase   =     bnb.nn.IntaParams(lowerCAmelCase__							,		requires_grad=lowerCAmelCase__							,		**lowerCAmelCase__							).to(lowerCAmelCase__							)
			elif is_abit:
				lowerCAmelCase   =     bnb.nn.Paramsabit(lowerCAmelCase__							,		requires_grad=lowerCAmelCase__							,		**lowerCAmelCase__							).to(lowerCAmelCase__							)

			lowerCAmelCase   =     new_value
			if fpaa_statistics is not None:
				setattr(module.weight							,		"""SCB"""							,		fpaa_statistics.to(lowerCAmelCase__							)							)

	else:
		if value is None:
			lowerCAmelCase   =     old_value.to(lowerCAmelCase__							)
		elif isinstance(lowerCAmelCase__							,		torch.Tensor							):
			lowerCAmelCase   =     value.to(lowerCAmelCase__							)
		else:
			lowerCAmelCase   =     torch.tensor(lowerCAmelCase__							,		device=lowerCAmelCase__							)

		if is_buffer:
			lowerCAmelCase   =     new_value
		else:
			lowerCAmelCase   =     nn.Parameter(lowerCAmelCase__							,		requires_grad=old_value.requires_grad							)
			lowerCAmelCase   =     new_value






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						str=None							,		_SCREAMING_SNAKE_CASE					:						List[str]=None							,		_SCREAMING_SNAKE_CASE					:						str=None							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]=False							)      ->						Optional[int]:







	"""simple docstring"""


	for name, module in model.named_children():
		if current_key_name is None:
			lowerCAmelCase   =     []
		current_key_name.append(lowerCAmelCase__							)

		if (isinstance(lowerCAmelCase__							,		nn.Linear							) or isinstance(lowerCAmelCase__							,		lowerCAmelCase__							)) and name not in modules_to_not_convert:
			# Check if the current key is not in the `modules_to_not_convert`
			if not any(key in """.""".join(lowerCAmelCase__							) for key in modules_to_not_convert							):
				with init_empty_weights():
					if isinstance(lowerCAmelCase__							,		lowerCAmelCase__							):
						lowerCAmelCase   =     module.weight.shape
					else:
						lowerCAmelCase   =     module.in_features
						lowerCAmelCase   =     module.out_features

					if quantization_config.quantization_method() == "llm_int8":
						lowerCAmelCase   =     bnb.nn.LinearabitLt(
						    lowerCAmelCase__							,		lowerCAmelCase__							,		module.bias is not None							,		has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight							,		threshold=quantization_config.llm_inta_threshold							,		)
						lowerCAmelCase   =     True
					else:
						if (
						    quantization_config.llm_inta_skip_modules is not None
						    and name in quantization_config.llm_inta_skip_modules
						):
							pass
						else:
							lowerCAmelCase   =     bnb.nn.Linearabit(
							    lowerCAmelCase__							,		lowerCAmelCase__							,		module.bias is not None							,		quantization_config.bnb_abit_compute_dtype							,		compress_statistics=quantization_config.bnb_abit_use_double_quant							,		quant_type=quantization_config.bnb_abit_quant_type							,		)
							lowerCAmelCase   =     True
                    # Store the module class in case we need to transpose the weight later
					lowerCAmelCase   =     type(lowerCAmelCase__							)
					# Force requires grad to False to avoid unexpected errors
					model._modules[name].requires_grad_(lowerCAmelCase__							)
		if len(list(module.children()							)							) > 0:
			lowerCAmelCase   =     _replace_with_bnb_linear(
			    lowerCAmelCase__							,		lowerCAmelCase__							,		lowerCAmelCase__							,		lowerCAmelCase__							,		has_been_replaced=lowerCAmelCase__							,		)
		# Remove the last key for recursion
		current_key_name.pop(-1							)
	return model, has_been_replaced






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						List[Any]=None							,		_SCREAMING_SNAKE_CASE					:						int=None							,		_SCREAMING_SNAKE_CASE					:						List[str]=None							)      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
	lowerCAmelCase   =     _replace_with_bnb_linear(
	    lowerCAmelCase__							,		lowerCAmelCase__							,		lowerCAmelCase__							,		lowerCAmelCase__							)

	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."""
		    """ Please double check your model architecture, or submit an issue on github if you think this is"""
		    """ a bug."""							)

	return model






def 		_snake_case		( *_SCREAMING_SNAKE_CASE					:						List[str]							,		**_SCREAMING_SNAKE_CASE					:						Any							)      ->						Optional[Any]:







	"""simple docstring"""
	warnings.warn(
	    """`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead"""							,		lowerCAmelCase__							,		)
	return replace_with_bnb_linear(*lowerCAmelCase__							,		**lowerCAmelCase__							)






def 		_snake_case		( *_SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		**_SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						Tuple:







	"""simple docstring"""
	warnings.warn(
	    """`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead"""							,		lowerCAmelCase__							,		)
	return set_module_quantized_tensor_to_device(*lowerCAmelCase__							,		**lowerCAmelCase__							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						List[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     deepcopy(lowerCAmelCase__							)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
	tied_model.tie_weights()

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

	# Check if it is a base model
	lowerCAmelCase   =     not hasattr(lowerCAmelCase__							,		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
	lowerCAmelCase   =     list(model.named_children()							)
	lowerCAmelCase   =     [list_modules[-1][0]]

	# add last module together with tied weights
	lowerCAmelCase   =     set(lowerCAmelCase__							) - set(lowerCAmelCase__							)
	lowerCAmelCase   =     list(set(lowerCAmelCase__							)							) + list(lowerCAmelCase__							)

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

	return filtered_module_names




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> bool:
		lowerCAmelCase   =     False

		if low == high:
			return swapped

		lowerCAmelCase   =     low
		lowerCAmelCase   =     high

		while left < right:
			if collection[left] > collection[right]:
				lowerCAmelCase,       lowerCAmelCase   =     (
				    collection[right],
				    collection[left],
				)
				lowerCAmelCase   =     True

			left += 1
			right -= 1

		if left == right and collection[left] > collection[right + 1]:
			lowerCAmelCase,       lowerCAmelCase   =     (
			    collection[right + 1],
			    collection[left],
			)

			lowerCAmelCase   =     True

		lowerCAmelCase   =     low + int((high - low) / 2							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


if __name__ == "__main__":
				UpperCAmelCase     		=   input('Enter numbers separated by a comma:\n').strip()
				UpperCAmelCase     		=   [int(item) for item in user_input.split(',')]
				print(circle_sort(unsorted))