






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 :    Union[str, Any]  							= logging.get_logger(__name__)




def _a (  SCREAMING_SNAKE_CASE__      :  Optional[Any]			,				SCREAMING_SNAKE_CASE__      :  List[str]			,				SCREAMING_SNAKE_CASE__      :  Dict			,				SCREAMING_SNAKE_CASE__      :  str=None			,				SCREAMING_SNAKE_CASE__      :  Tuple=None       )	->    List[str]:

							'''simple docstring'''






							if "." in tensor_name:
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tensor_name.split("."       )
														for split in splits[:-1]:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						getattr(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
																					if new_module is None:
																												raise ValueError(f'''{module} has no attribute {split}.'''       )
																					SCREAMING_SNAKE_CASE__       :      str     =						new_module
														SCREAMING_SNAKE_CASE__       :      Any     =						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}.'''       )
							SCREAMING_SNAKE_CASE__       :      int     =						tensor_name in module._buffers
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						getattr(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							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}.'''       )

							SCREAMING_SNAKE_CASE__       :      Optional[int]     =						False
							SCREAMING_SNAKE_CASE__       :      str     =						False
							if is_buffer or not is_bitsandbytes_available():
														SCREAMING_SNAKE_CASE__       :      Any     =						False
														SCREAMING_SNAKE_CASE__       :      Any     =						False
							else:
														SCREAMING_SNAKE_CASE__       :      str     =						hasattr(bnb.nn			,				"Params4bit"       ) and isinstance(module._parameters[tensor_name]			,				bnb.nn.Paramsabit       )
														SCREAMING_SNAKE_CASE__       :      Tuple     =						isinstance(module._parameters[tensor_name]			,				bnb.nn.IntaParams       )

							if is_abit or is_abit:
														SCREAMING_SNAKE_CASE__       :      int     =						module._parameters[tensor_name]
														if param.device.type != "cuda":
																					if value is None:
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						old_value.to(SCREAMING_SNAKE_CASE__       )
																					elif isinstance(SCREAMING_SNAKE_CASE__			,				torch.Tensor       ):
																												SCREAMING_SNAKE_CASE__       :      int     =						value.to("cpu"       )
																												if value.dtype == torch.inta:
																																			SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						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:
																												SCREAMING_SNAKE_CASE__       :      Any     =						torch.tensor(SCREAMING_SNAKE_CASE__			,				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			,				SCREAMING_SNAKE_CASE__       ) and fpaa_statistics is None:
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						new_value.T

																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						old_value.__dict__
																					if is_abit:
																												SCREAMING_SNAKE_CASE__       :      Any     =						bnb.nn.IntaParams(SCREAMING_SNAKE_CASE__			,				requires_grad=SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )
																					elif is_abit:
																												SCREAMING_SNAKE_CASE__       :      Any     =						bnb.nn.Paramsabit(SCREAMING_SNAKE_CASE__			,				requires_grad=SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       ).to(SCREAMING_SNAKE_CASE__       )

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

							else:
														if value is None:
																					SCREAMING_SNAKE_CASE__       :      str     =						old_value.to(SCREAMING_SNAKE_CASE__       )
														elif isinstance(SCREAMING_SNAKE_CASE__			,				torch.Tensor       ):
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						value.to(SCREAMING_SNAKE_CASE__       )
														else:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						torch.tensor(SCREAMING_SNAKE_CASE__			,				device=SCREAMING_SNAKE_CASE__       )

														if is_buffer:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_value
														else:
																					SCREAMING_SNAKE_CASE__       :      int     =						nn.Parameter(SCREAMING_SNAKE_CASE__			,				requires_grad=old_value.requires_grad       )
																					SCREAMING_SNAKE_CASE__       :      int     =						new_value




def _a (  SCREAMING_SNAKE_CASE__      :  Any			,				SCREAMING_SNAKE_CASE__      :  int=None			,				SCREAMING_SNAKE_CASE__      :  Optional[Any]=None			,				SCREAMING_SNAKE_CASE__      :  Dict=None			,				SCREAMING_SNAKE_CASE__      :  Tuple=False       )	->    Union[str, Any]:

							'''simple docstring'''






							for name, module in model.named_children():
														if current_key_name is None:
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[]
														current_key_name.append(SCREAMING_SNAKE_CASE__       )

														if (isinstance(SCREAMING_SNAKE_CASE__			,				nn.Linear       ) or isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )) 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(SCREAMING_SNAKE_CASE__       ) for key in modules_to_not_convert       ):
																												with init_empty_weights():
																																			if isinstance(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       ):
																																										SCREAMING_SNAKE_CASE__       :      Optional[int]     =						module.weight.shape
																																			else:
																																										SCREAMING_SNAKE_CASE__       :      Dict     =						module.in_features
																																										SCREAMING_SNAKE_CASE__       :      Dict     =						module.out_features

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




def _a (  SCREAMING_SNAKE_CASE__      :  Tuple			,				SCREAMING_SNAKE_CASE__      :  Tuple=None			,				SCREAMING_SNAKE_CASE__      :  Optional[int]=None			,				SCREAMING_SNAKE_CASE__      :  Union[str, Any]=None       )	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
							SCREAMING_SNAKE_CASE__       :      List[str]     =						_replace_with_bnb_linear(
							    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )

							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 (  *SCREAMING_SNAKE_CASE__      :  Any			,				**SCREAMING_SNAKE_CASE__      :  List[str]       )	->    List[str]:

							'''simple docstring'''






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




def _a (  *SCREAMING_SNAKE_CASE__      :  Dict			,				**SCREAMING_SNAKE_CASE__      :  Tuple       )	->    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"			,				SCREAMING_SNAKE_CASE__			,				)
							return set_module_quantized_tensor_to_device(*SCREAMING_SNAKE_CASE__			,				**SCREAMING_SNAKE_CASE__       )







def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    List[str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      List[Any]     =						deepcopy(SCREAMING_SNAKE_CASE__       )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
							tied_model.tie_weights()

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

							# Check if it is a base model
							SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						not hasattr(SCREAMING_SNAKE_CASE__			,				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
							SCREAMING_SNAKE_CASE__       :      str     =						list(model.named_children()       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[list_modules[-1][0]]

							# add last module together with tied weights
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						set(SCREAMING_SNAKE_CASE__       ) - set(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						list(set(SCREAMING_SNAKE_CASE__       )       ) + list(SCREAMING_SNAKE_CASE__       )

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

							return filtered_module_names













from queue import PriorityQueue
from typing import Any

import numpy as np




def _a (  SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  set			,				SCREAMING_SNAKE_CASE__      :  set			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  PriorityQueue			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  float | int			,				)	->    float | int:

							'''simple docstring'''






							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						cst_fwd.get(SCREAMING_SNAKE_CASE__			,				np.inf       )
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)       )
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_cost_f
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  dict			,				SCREAMING_SNAKE_CASE__      :  dict       )	->    int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						-1

							SCREAMING_SNAKE_CASE__       :      List[str]     =						set()
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						set()
							SCREAMING_SNAKE_CASE__       :      int     =						{source: 0}
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{destination: 0}
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						{source: None}
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						{destination: None}
							SCREAMING_SNAKE_CASE__       :      PriorityQueue[Any]     =						PriorityQueue()
							SCREAMING_SNAKE_CASE__       :      PriorityQueue[Any]     =						PriorityQueue()

							SCREAMING_SNAKE_CASE__       :      Dict     =						np.inf

							queue_forward.put((0, source)       )
							queue_backward.put((0, destination)       )

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						queue_forward.get()
														visited_forward.add(SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						queue_backward.get()
														visited_backward.add(SCREAMING_SNAKE_CASE__       )

														SCREAMING_SNAKE_CASE__       :      Any     =						pass_and_relaxation(
														    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						pass_and_relaxation(
														    SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__			,				)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE__       :      int     =						shortest_distance
							return shortest_path_distance


_lowerCamelCase :    Optional[Any]  							= {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
_lowerCamelCase :    Tuple  							= {
    '''B''': [['''E''', 1]],
    '''C''': [['''B''', 1]],
    '''D''': [['''C''', 1]],
    '''F''': [['''D''', 1], ['''G''', 1]],
    '''E''': [[None, np.inf]],
    '''G''': [['''E''', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()






