






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


UpperCamelCase			    =     logging.getLogger(__name__)

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__ = None	,snake_case__ = None	,snake_case__ = None	,snake_case__ = None	,snake_case__ = None	,snake_case__ = False	,):






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  bnb_quantization_config.load_in_abit
						_SCREAMING_SNAKE_CASE     				=  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."""		)

						_SCREAMING_SNAKE_CASE     				=  []
						# custom device map
						if isinstance(__A	,__A		) and len(device_map.keys()		) > 1:
												_SCREAMING_SNAKE_CASE     				=  [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:
												_SCREAMING_SNAKE_CASE     				=  get_keys_to_not_convert(__A		)

						# add cpu modules to skip modules only for 4-bit modules
						if load_in_abit:
												bnb_quantization_config.skip_modules.extend(__A		)
						_SCREAMING_SNAKE_CASE     				=  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:
												_SCREAMING_SNAKE_CASE     				=  []
						_SCREAMING_SNAKE_CASE     				=  bnb_quantization_config.keep_in_fpaa_modules
						modules_to_not_convert.extend(__A		)

						# compatibility with peft
						_SCREAMING_SNAKE_CASE     				=  load_in_abit
						_SCREAMING_SNAKE_CASE     				=  load_in_abit

						_SCREAMING_SNAKE_CASE     				=  get_parameter_device(__A		)
						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."""		)
												_SCREAMING_SNAKE_CASE     				=  replace_with_bnb_layers(__A	,__A	,modules_to_not_convert=__A		)
												# convert param to the right dtype
												_SCREAMING_SNAKE_CASE     				=  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:
																														_SCREAMING_SNAKE_CASE     				=  name.replace(""".weight"""	,""""""		).replace(""".bias"""	,""""""		)
																														_SCREAMING_SNAKE_CASE     				=  getattr(__A	,__A	,__A		)
																														if param is not None:
																																				param.to(torch.floataa		)
																		elif torch.is_floating_point(__A		):
																								param.to(__A		)
												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():
																		_SCREAMING_SNAKE_CASE     				=  replace_with_bnb_layers(
																		    __A	,__A	,modules_to_not_convert=__A		)

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

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

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

def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__=None	,snake_case__=None	,snake_case__=None		):






						"""simple docstring"""





						if device_map is None:
												if torch.cuda.is_available():
																		_SCREAMING_SNAKE_CASE     				=  {"""""": 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(__A	,__A		):
												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\'."""		)

												_SCREAMING_SNAKE_CASE     				=  {}
												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		)
												    }		)

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

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

												_SCREAMING_SNAKE_CASE     				=  max_memory
												_SCREAMING_SNAKE_CASE     				=  infer_auto_device_map(__A	,**__A		)

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

												_SCREAMING_SNAKE_CASE     				=  {
												    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      (		snake_case__	,snake_case__	,snake_case__=None	,snake_case__=None		):






						"""simple docstring"""





						if modules_to_not_convert is None:
												_SCREAMING_SNAKE_CASE     				=  []

						_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  _replace_with_bnb_layers(
						    __A	,__A	,__A	,__A		)
						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      (		snake_case__	,snake_case__	,snake_case__=None	,snake_case__=None	,):






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  False
						for name, module in model.named_children():
												if current_key_name is None:
																		_SCREAMING_SNAKE_CASE     				=  []
												current_key_name.append(__A		)
												if isinstance(__A	,nn.Linear		) and name not in modules_to_not_convert:
																		# Check if the current key is not in the `modules_to_not_convert`
																		_SCREAMING_SNAKE_CASE     				=  """.""".join(__A		)
																		_SCREAMING_SNAKE_CASE     				=  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:
																														_SCREAMING_SNAKE_CASE     				=  False
																														break
																		if proceed:
																								# Load bnb module with empty weight and replace ``nn.Linear` module
																								if bnb_quantization_config.load_in_abit:
																														_SCREAMING_SNAKE_CASE     				=  bnb.nn.LinearabitLt(
																														    module.in_features	,module.out_features	,module.bias is not None	,has_fpaa_weights=__A	,threshold=bnb_quantization_config.llm_inta_threshold	,)
																								elif bnb_quantization_config.load_in_abit:
																														_SCREAMING_SNAKE_CASE     				=  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"""		)
																								_SCREAMING_SNAKE_CASE     				=  module.weight.data
																								if module.bias is not None:
																														_SCREAMING_SNAKE_CASE     				=  module.bias.data
																								bnb_module.requires_grad_(__A		)
																								setattr(__A	,__A	,__A		)
																								_SCREAMING_SNAKE_CASE     				=  True
												if len(list(module.children()		)		) > 0:
																		_SCREAMING_SNAKE_CASE				,					_SCREAMING_SNAKE_CASE     				=  _replace_with_bnb_layers(
																		    __A	,__A	,__A	,__A		)
																		_SCREAMING_SNAKE_CASE     				=  has_been_replaced | _has_been_replaced
												# Remove the last key for recursion
												current_key_name.pop(-1		)
						return model, has_been_replaced

def 	__lowerCamelCase      (		snake_case__		):






						"""simple docstring"""





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

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

						# Check if it is a base model
						_SCREAMING_SNAKE_CASE     				=  False
						if hasattr(__A	,"""base_model_prefix"""		):
												_SCREAMING_SNAKE_CASE     				=  not hasattr(__A	,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     				=  list(model.named_children()		)
						_SCREAMING_SNAKE_CASE     				=  [list_modules[-1][0]]

						# add last module together with tied weights
						_SCREAMING_SNAKE_CASE     				=  set(__A		) - set(__A		)
						_SCREAMING_SNAKE_CASE     				=  list(set(__A		)		) + list(__A		)

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

						return filtered_module_names

def 	__lowerCamelCase      (		snake_case__		):






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		):






						"""simple docstring"""





						return next(parameter.parameters()		).device




def 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__	,snake_case__		):






						"""simple docstring"""





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

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase			    =     {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3TokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LayoutLMv3ForQuestionAnswering''',
		    '''LayoutLMv3ForSequenceClassification''',
		    '''LayoutLMv3ForTokenClassification''',
		    '''LayoutLMv3Model''',
		    '''LayoutLMv3PreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     [
		    '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFLayoutLMv3ForQuestionAnswering''',
		    '''TFLayoutLMv3ForSequenceClassification''',
		    '''TFLayoutLMv3ForTokenClassification''',
		    '''TFLayoutLMv3Model''',
		    '''TFLayoutLMv3PreTrainedModel''',
		]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''LayoutLMv3FeatureExtractor''']
		UpperCamelCase			    =     ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
		from .configuration_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    LayoutLMvaConfig,
		    LayoutLMvaOnnxConfig,
		)
		from .processing_layoutlmva import LayoutLMvaProcessor
		from .tokenization_layoutlmva import LayoutLMvaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LayoutLMvaForQuestionAnswering,
				    LayoutLMvaForSequenceClassification,
				    LayoutLMvaForTokenClassification,
				    LayoutLMvaModel,
				    LayoutLMvaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_layoutlmva import (
				    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFLayoutLMvaForQuestionAnswering,
				    TFLayoutLMvaForSequenceClassification,
				    TFLayoutLMvaForTokenClassification,
				    TFLayoutLMvaModel,
				    TFLayoutLMvaPreTrainedModel,
				)

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
				from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
		import sys

		UpperCamelCase			    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

