

'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Union[str, Any]=   "mgp-str"


							def __init__(			self    ,			_snake_case=[32, 128]    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=27    ,			_snake_case=38    ,			_snake_case=5_0257    ,			_snake_case=3_0522    ,			_snake_case=768    ,			_snake_case=12    ,			_snake_case=12    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=False    ,			_snake_case=1e-5    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=False    ,			_snake_case=0.02    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Any       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Union[str, Any]       	=					max_token_length
								UpperCAmelCase_		:      Tuple       	=					num_character_labels
								UpperCAmelCase_		:      Any       	=					num_bpe_labels
								UpperCAmelCase_		:      Dict       	=					num_wordpiece_labels
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_size
								UpperCAmelCase_		:      Optional[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Any       	=					num_attention_heads
								UpperCAmelCase_		:      Tuple       	=					mlp_ratio
								UpperCAmelCase_		:      List[Any]       	=					distilled
								UpperCAmelCase_		:      Union[str, Any]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[Any]       	=					drop_rate
								UpperCAmelCase_		:      Optional[int]       	=					qkv_bias
								UpperCAmelCase_		:      Tuple       	=					attn_drop_rate
								UpperCAmelCase_		:      Union[str, Any]       	=					drop_path_rate
								UpperCAmelCase_		:      Dict       	=					output_aa_attentions
								UpperCAmelCase_		:      Tuple       	=					initializer_range









'''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

lowerCAmelCase__										= logging.get_logger(__name__)







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None  )    ->    Any:
	# Recurse if needed
	if "." in tensor_name:
		UpperCAmelCase_		:      Tuple       	=					tensor_name.split('.'  )
		for split in splits[:-1]:
			UpperCAmelCase_		:      Optional[int]       	=					getattr(UpperCamelCase   ,UpperCamelCase  )
			if new_module is None:
				raise ValueError(f"""{module} has no attribute {split}."""  )
			UpperCAmelCase_		:      Union[str, Any]       	=					new_module
		UpperCAmelCase_		:      int       	=					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_		:      Tuple       	=					tensor_name in module._buffers
	UpperCAmelCase_		:      Any       	=					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}."""  )

	UpperCAmelCase_		:      Any       	=					False
	UpperCAmelCase_		:      Any       	=					False
	if is_buffer or not is_bitsandbytes_available():
		UpperCAmelCase_		:      Dict       	=					False
		UpperCAmelCase_		:      Tuple       	=					False
	else:
		UpperCAmelCase_		:      List[str]       	=					hasattr(bnb.nn   ,'Params4bit'  ) and isinstance(module._parameters[tensor_name]   ,bnb.nn.Paramsabit  )
		UpperCAmelCase_		:      str       	=					isinstance(module._parameters[tensor_name]   ,bnb.nn.IntaParams  )

	if is_abit or is_abit:
		UpperCAmelCase_		:      Union[str, Any]       	=					module._parameters[tensor_name]
		if param.device.type != "cuda":
			if value is None:
				UpperCAmelCase_		:      List[str]       	=					old_value.to(UpperCamelCase  )
			elif isinstance(UpperCamelCase   ,torch.Tensor  ):
				UpperCAmelCase_		:      Any       	=					value.to('cpu'  )
				if value.dtype == torch.inta:
					UpperCAmelCase_		:      Dict       	=					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_		:      List[Any]       	=					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:
				UpperCAmelCase_		:      Dict       	=					new_value.T

			UpperCAmelCase_		:      Tuple       	=					old_value.__dict__
			if is_abit:
				UpperCAmelCase_		:      Any       	=					bnb.nn.IntaParams(UpperCamelCase   ,requires_grad=UpperCamelCase   ,**UpperCamelCase  ).to(UpperCamelCase  )
			elif is_abit:
				UpperCAmelCase_		:      Optional[Any]       	=					bnb.nn.Paramsabit(UpperCamelCase   ,requires_grad=UpperCamelCase   ,**UpperCamelCase  ).to(UpperCamelCase  )

			UpperCAmelCase_		:      Optional[Any]       	=					new_value
			if fpaa_statistics is not None:
				setattr(module.weight   ,'SCB'   ,fpaa_statistics.to(UpperCamelCase  )  )

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

		if is_buffer:
			UpperCAmelCase_		:      Dict       	=					new_value
		else:
			UpperCAmelCase_		:      Optional[int]       	=					nn.Parameter(UpperCamelCase   ,requires_grad=old_value.requires_grad  )
			UpperCAmelCase_		:      Optional[int]       	=					new_value







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=False  )    ->    Tuple:
	for name, module in model.named_children():
		if current_key_name is None:
			UpperCAmelCase_		:      Optional[int]       	=					[]
		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  ):
						UpperCAmelCase_	,    UpperCAmelCase_		:      Optional[Any]       	=					module.weight.shape
					else:
						UpperCAmelCase_		:      Dict       	=					module.in_features
						UpperCAmelCase_		:      Any       	=					module.out_features

					if quantization_config.quantization_method() == "llm_int8":
						UpperCAmelCase_		:      Optional[int]       	=					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   ,)
						UpperCAmelCase_		:      List[Any]       	=					True
					else:
						if (
						    quantization_config.llm_inta_skip_modules is not None
						    and name in quantization_config.llm_inta_skip_modules
						):
							pass
						else:
							UpperCAmelCase_		:      Union[str, Any]       	=					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   ,)
							UpperCAmelCase_		:      Dict       	=					True
                    # Store the module class in case we need to transpose the weight later
					UpperCAmelCase_		:      List[str]       	=					type(UpperCamelCase  )
					# Force requires grad to False to avoid unexpected errors
					model._modules[name].requires_grad_(UpperCamelCase  )
		if len(list(module.children()  )  ) > 0:
			UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					_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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase=None   ,UpperCamelCase=None   ,UpperCamelCase=None  )    ->    Optional[int]:
	UpperCAmelCase_		:      Union[str, Any]       	=					['lm_head'] if modules_to_not_convert is None else modules_to_not_convert
	UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					_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 						SCREAMING_SNAKE_CASE(  *UpperCamelCase   ,**UpperCamelCase  )    ->    Tuple:
	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 						SCREAMING_SNAKE_CASE(  *UpperCamelCase   ,**UpperCamelCase  )    ->    List[Any]:
	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 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    int:
	UpperCAmelCase_		:      Union[str, Any]       	=					deepcopy(UpperCamelCase  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`
	tied_model.tie_weights()

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

	# Check if it is a base model
	UpperCAmelCase_		:      List[Any]       	=					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
	UpperCAmelCase_		:      str       	=					list(model.named_children()  )
	UpperCAmelCase_		:      Optional[int]       	=					[list_modules[-1][0]]

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

	# remove ".weight" from the keys
	UpperCAmelCase_		:      Optional[int]       	=					['.weight', '.bias']
	UpperCAmelCase_		:      Tuple       	=					[]
	for name in list_untouched:
		for name_to_remove in names_to_remove:
			if name_to_remove in name:
				UpperCAmelCase_		:      Union[str, Any]       	=					name.replace(UpperCamelCase   ,''  )
		filtered_module_names.append(UpperCamelCase  )

	return filtered_module_names






