






import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
		from ... import PreTrainedTokenizerBase, TensorType

__magic_name__:	Tuple							=      logging.get_logger(__name__)





class 			snake_case__   (				UpperCAmelCase__ ):
	lowercase__		:	Union[str, Any]				 =       '''vision-encoder-decoder'''
	lowercase__		:	List[Any]				 =       True
	def __init__(							self     ,  **lowerCAmelCase__					)				-> Optional[Any]:
							super().__init__(**UpperCAmelCase__					)
							if "encoder" not in kwargs or "decoder" not in kwargs:
													raise ValueError(
													    F'A configuraton of type {self.model_type} cannot be instantiated because '
													    F'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'					)

							__magic_name__     :  Tuple  		=   kwargs.pop("""encoder"""					)
							__magic_name__     :  Any  		=   encoder_config.pop("""model_type"""					)
							__magic_name__     :  Optional[int]  		=   kwargs.pop("""decoder"""					)
							__magic_name__     :  List[Any]  		=   decoder_config.pop("""model_type"""					)

							__magic_name__     :  Union[str, Any]  		=   AutoConfig.for_model(UpperCAmelCase__     ,  **UpperCAmelCase__					)
							__magic_name__     :  Any  		=   AutoConfig.for_model(UpperCAmelCase__     ,  **UpperCAmelCase__					)
							__magic_name__     :  Tuple  		=   True
	@classmethod
	def      __magic_name__							(							cls     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  **lowerCAmelCase__					)				-> PretrainedConfig:
							logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""					)
							__magic_name__     :  Dict  		=   True
							__magic_name__     :  Any  		=   True

							return cls(encoder=encoder_config.to_dict()     ,  decoder=decoder_config.to_dict()     ,  **UpperCAmelCase__					)







	def      __magic_name__							(							self					)				-> List[str]:
							__magic_name__     :  Any  		=   copy.deepcopy(self.__dict__					)
							__magic_name__     :  Dict  		=   self.encoder.to_dict()
							__magic_name__     :  Any  		=   self.decoder.to_dict()
							__magic_name__     :  Tuple  		=   self.__class__.model_type
							return output





class 			snake_case__   (				UpperCAmelCase__ ):
	lowercase__		:	List[str]				 =       version.parse('''1.11''' )
	@property
	def      __magic_name__							(							self					)				-> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]					)
	@property
	def      __magic_name__							(							self					)				-> float:
							return 1e-4







	@property
	def      __magic_name__							(							self					)				-> Mapping[str, Mapping[int, str]]:
							return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}					)





class 			snake_case__   (				UpperCAmelCase__ ):
	@property
	def      __magic_name__							(							self					)				-> Mapping[str, Mapping[int, str]]:
							__magic_name__     :  List[str]  		=   OrderedDict()
							__magic_name__     :  List[str]  		=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
							__magic_name__     :  int  		=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
							__magic_name__     :  Union[str, Any]  		=   {0: """batch""", 1: """encoder_sequence"""}

							return common_inputs







	def      __magic_name__							(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = -1     ,  lowerCAmelCase__ = -1     ,  lowerCAmelCase__ = False     ,  lowerCAmelCase__ = None     ,  )				-> Mapping[str, Any]:
							import torch

							__magic_name__     :  Any  		=   OrderedDict()

							__magic_name__     :  List[str]  		=   super().generate_dummy_inputs(
							    UpperCAmelCase__     ,  batch_size=UpperCAmelCase__     ,  seq_length=UpperCAmelCase__     ,  is_pair=UpperCAmelCase__     ,  framework=UpperCAmelCase__					)

							__magic_name__		,__magic_name__     :  Dict  		=   dummy_input["""input_ids"""].shape
							__magic_name__     :  Any  		=   (batch, encoder_sequence, self._config.encoder_hidden_size)
							__magic_name__     :  List[Any]  		=   dummy_input.pop("""input_ids"""					)
							__magic_name__     :  List[Any]  		=   dummy_input.pop("""attention_mask"""					)
							__magic_name__     :  Dict  		=   torch.zeros(UpperCAmelCase__					)

							return common_inputs





class 			snake_case__   (				UpperCAmelCase__ ):
	@property
	def      __magic_name__							(							self					)				-> None:
							pass
	def      __magic_name__							(							self     ,  lowerCAmelCase__					)				-> OnnxConfig:
							return VisionEncoderDecoderEncoderOnnxConfig(UpperCAmelCase__					)







	def      __magic_name__							(							self     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__ = "default"					)				-> OnnxConfig:
							__magic_name__     :  int  		=   encoder_config.hidden_size
							return VisionEncoderDecoderDecoderOnnxConfig(UpperCAmelCase__     ,  UpperCAmelCase__					)






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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_=None					,							lowercase_=None		)  ->			Dict:



       """simple docstring"""

       if "." in tensor_name:
              A__ 					=       tensor_name.split('''.'''		)
              for split in splits[:-1]:
                     A__ 					=       getattr(lowercase_					,							lowercase_		)
                     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(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}."""		)

       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(lowercase_		)
                     elif isinstance(lowercase_					,							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(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:
                            A__ 					=       new_value.T

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

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

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

              if is_buffer:
                     A__ 					=       new_value
              else:
                     A__ 					=       nn.Parameter(lowercase_					,							requires_grad=old_value.requires_grad		)
                     A__ 					=       new_value







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=None					,							lowercase_=None					,							lowercase_=None					,							lowercase_=False		)  ->			Dict:



       """simple docstring"""

       for name, module in model.named_children():
              if current_key_name is None:
                     A__ 					=       []
              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_		):
                                          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(
                                              lowercase_					,							lowercase_					,							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(
                                                     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					,							)
                                                 A__ 					=       True
                    # Store the module class in case we need to transpose the weight later
                                   A__ 					=       type(lowercase_		)
                                   # Force requires grad to False to avoid unexpected errors
                                   model._modules[name].requires_grad_(lowercase_		)
              if len(list(module.children()		)		) > 0:
                     A__					,				A__ 					=       _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 SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_=None					,							lowercase_=None					,							lowercase_=None		)  ->			Tuple:



       """simple docstring"""

       A__ 					=       ['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
       A__					,				A__ 					=       _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 SCREAMING_SNAKE_CASE							(			*lowercase_					,							**lowercase_		)  ->			Dict:



       """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 SCREAMING_SNAKE_CASE							(			*lowercase_					,							**lowercase_		)  ->			Optional[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 SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			List[str]:



       """simple docstring"""

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

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

       # Check if it is a base model
       A__ 					=       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
       A__ 					=       list(model.named_children()		)
       A__ 					=       [list_modules[-1][0]]

       # add last module together with tied weights
       A__ 					=       set(lowercase_		) - set(lowercase_		)
       A__ 					=       list(set(lowercase_		)		) + list(lowercase_		)

       # 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(lowercase_					,							''''''		)
              filtered_module_names.append(lowercase_		)

       return filtered_module_names


