
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					: str      =       logging.get_logger(__name__)


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase=None    ,       lowercase=None )   ->  Optional[Any]:
		'''simple docstring'''

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

		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}.''' )

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

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

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

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

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

				if is_buffer:
						UpperCamelCase		=     new_value
				else:
						UpperCamelCase		=     nn.Parameter(lowercase    ,       requires_grad=old_value.requires_grad )
						UpperCamelCase		=     new_value


def       A       (					lowercase    ,       lowercase=None    ,       lowercase=None    ,       lowercase=None    ,       lowercase=False )   ->  str:
		'''simple docstring'''

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

				if (isinstance(lowercase    ,       nn.Linear ) or isinstance(lowercase    ,       lowercase )) 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(lowercase ) for key in modules_to_not_convert ):
								with init_empty_weights():
										if isinstance(lowercase    ,       lowercase ):
												UpperCamelCase	,  UpperCamelCase		=     module.weight.shape
										else:
												UpperCamelCase		=     module.in_features
												UpperCamelCase		=     module.out_features

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


def       A       (					lowercase    ,       lowercase=None    ,       lowercase=None    ,       lowercase=None )   ->  List[str]:
		'''simple docstring'''

		UpperCamelCase		=     ['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
		UpperCamelCase	,  UpperCamelCase		=     _replace_with_bnb_linear(
		    lowercase    ,       lowercase    ,       lowercase    ,       lowercase )

		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       A       (					*lowercase    ,       **lowercase )   ->  str:
		'''simple docstring'''

		warnings.warn(
		    '`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'    ,       lowercase    ,       )
		return replace_with_bnb_linear(*lowercase    ,       **lowercase )


def       A       (					*lowercase    ,       **lowercase )   ->  Union[str, Any]:
		'''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'    ,       lowercase    ,       )
		return set_module_quantized_tensor_to_device(*lowercase    ,       **lowercase )


def       A       (					lowercase )   ->  List[str]:
		'''simple docstring'''

		UpperCamelCase		=     deepcopy(lowercase )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
		tied_model.tie_weights()

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

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

		# add last module together with tied weights
		UpperCamelCase		=     set(lowercase ) - set(lowercase )
		UpperCamelCase		=     list(set(lowercase ) ) + list(lowercase )

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

		return filtered_module_names


from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
