






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

lowerCAmelCase_           = logging.get_logger(__name__)







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       Optional[Any],						UpperCAmelCase	:       int,						UpperCAmelCase	:       Tuple=None,						UpperCAmelCase	:       Any=None   ):
				# Recurse if needed
				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]

				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}.'   )
				A	 =      tensor_name in module._buffers
				A	 =      getattr(UpperCAmelCase,						UpperCAmelCase   )

				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}.'   )

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

				if is_abit or is_abit:
								A	 =      module._parameters[tensor_name]
								if param.device.type != "cuda":
												if value is None:
																A	 =      old_value.to(UpperCAmelCase   )
												elif isinstance(UpperCAmelCase,						torch.Tensor   ):
																A	 =      value.to('cpu'   )
																if value.dtype == torch.inta:
																				A	 =      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:
																A	 =      torch.tensor(UpperCAmelCase,						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,						UpperCAmelCase   ) and fpaa_statistics is None:
																A	 =      new_value.T

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

												A	 =      new_value
												if fpaa_statistics is not None:
																setattr(module.weight,						'SCB',						fpaa_statistics.to(UpperCAmelCase   )   )

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

								if is_buffer:
												A	 =      new_value
								else:
												A	 =      nn.Parameter(UpperCAmelCase,						requires_grad=old_value.requires_grad   )
												A	 =      new_value







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any]=None,						UpperCAmelCase	:       List[str]=None,						UpperCAmelCase	:       Any=None,						UpperCAmelCase	:       Tuple=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   ) or isinstance(UpperCAmelCase,						UpperCAmelCase   )) 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(UpperCAmelCase   ) for key in modules_to_not_convert   ):
																with init_empty_weights():
																				if isinstance(UpperCAmelCase,						UpperCAmelCase   ):
																								A ,		A	 =      module.weight.shape
																				else:
																								A	 =      module.in_features
																								A	 =      module.out_features

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







def      snake_case		(							UpperCAmelCase	:       List[str],						UpperCAmelCase	:       Optional[Any]=None,						UpperCAmelCase	:       Tuple=None,						UpperCAmelCase	:       int=None   ):
				A	 =      ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
				A ,		A	 =      _replace_with_bnb_linear(
				    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.'
								    ' Please double check your model architecture, or submit an issue on github if you think this is'
								    ' a bug.'   )

				return model







def      snake_case		(							*UpperCAmelCase	:       Dict,						**UpperCAmelCase	:       List[str]   ):
				warnings.warn(
				    '`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead',						UpperCAmelCase,						)
				return replace_with_bnb_linear(*UpperCAmelCase,						**UpperCAmelCase   )







def      snake_case		(							*UpperCAmelCase	:       Any,						**UpperCAmelCase	:       Tuple   ):
				warnings.warn(
				    '`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead',						UpperCAmelCase,						)
				return set_module_quantized_tensor_to_device(*UpperCAmelCase,						**UpperCAmelCase   )







def      snake_case		(							UpperCAmelCase	:       Tuple   ):
				A	 =      deepcopy(UpperCAmelCase   )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
				tied_model.tie_weights()

				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	 =      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













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


if __name__ == "__main__":
					lowerCAmelCase_           = input('Enter numbers separated by a comma:\n').strip()
					lowerCAmelCase_           = [int(item) for item in user_input.split(',')]
					lowerCAmelCase_           = int(input('Enter the number to be searched:\n'))
					lowerCAmelCase_           = jump_search(arr, x)
					if res == -1:
										print('Number not found!')
					else:
										print(f'''Number {x} is at index {res}''')





