






"""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_	:    List[Any]            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None		):







    '''simple docstring'''
    # Recurse if needed
    if "." in tensor_name:
        UpperCAmelCase	    =			tensor_name.split("""."""		)
        for split in splits[:-1]:
            UpperCAmelCase	    =			getattr(lowerCAmelCase	,      lowerCAmelCase		)
            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(lowerCAmelCase	,      lowerCAmelCase		)

    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(lowerCAmelCase		)
            elif isinstance(lowerCAmelCase	,      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(lowerCAmelCase	,      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	,      lowerCAmelCase		) and fpaa_statistics is None:
                UpperCAmelCase	    =			new_value.T

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

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

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

        if is_buffer:
            UpperCAmelCase	    =			new_value
        else:
            UpperCAmelCase	    =			nn.Parameter(lowerCAmelCase	,      requires_grad=old_value.requires_grad		)
            UpperCAmelCase	    =			new_value






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=False		):







    '''simple docstring'''
    for name, module in model.named_children():
        if current_key_name is None:
            UpperCAmelCase	    =			[]
        current_key_name.append(lowerCAmelCase		)

        if (isinstance(lowerCAmelCase	,      nn.Linear		) or isinstance(lowerCAmelCase	,      lowerCAmelCase		)) 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(lowerCAmelCase		) for key in modules_to_not_convert		):
                with init_empty_weights():
                    if isinstance(lowerCAmelCase	,      lowerCAmelCase		):
                        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(
                            lowerCAmelCase	,      lowerCAmelCase	,      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(
                                lowerCAmelCase	,      lowerCAmelCase	,      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(lowerCAmelCase		)
                    # Force requires grad to False to avoid unexpected errors
                    model._modules[name].requires_grad_(lowerCAmelCase		)
        if len(list(module.children()		)		) > 0:
            UpperCAmelCase    ,       UpperCAmelCase	    =			_replace_with_bnb_linear(
                lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      has_been_replaced=lowerCAmelCase	,      )
        # Remove the last key for recursion
        current_key_name.pop(-1		)
    return model, has_been_replaced






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase=None	,      lowerCAmelCase=None	,      lowerCAmelCase=None		):







    '''simple docstring'''
    UpperCAmelCase	    =			["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
    UpperCAmelCase    ,       UpperCAmelCase	    =			_replace_with_bnb_linear(
        lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)

    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       _lowerCAmelCase       (      *lowerCAmelCase	,      **lowerCAmelCase		):







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






def       _lowerCAmelCase       (      *lowerCAmelCase	,      **lowerCAmelCase		):







    '''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"""	,      lowerCAmelCase	,      )
    return set_module_quantized_tensor_to_device(*lowerCAmelCase	,      **lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase		):







    '''simple docstring'''
    UpperCAmelCase	    =			deepcopy(lowerCAmelCase		)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
    tied_model.tie_weights()

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

    # Check if it is a base model
    UpperCAmelCase	    =			not hasattr(lowerCAmelCase	,      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(lowerCAmelCase		) - set(lowerCAmelCase		)
    UpperCAmelCase	    =			list(set(lowerCAmelCase		)		) + list(lowerCAmelCase		)

    # 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(lowerCAmelCase	,      """"""		)
        filtered_module_names.append(lowerCAmelCase		)

    return filtered_module_names








"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_	:    int            =      logging.get_logger(__name__)

lowerCAmelCase_	:    List[Any]            =      {
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'xlm-roberta-xl'



  def __init__(      self ,		snake_case__=25_08_80 ,		snake_case__=25_60 ,		snake_case__=36 ,		snake_case__=32 ,		snake_case__=1_02_40 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=5_14 ,		snake_case__=1 ,		snake_case__=0.02 ,		snake_case__=1e-05 ,		snake_case__=1 ,		snake_case__=0 ,		snake_case__=2 ,		snake_case__="absolute" ,		snake_case__=True ,		snake_case__=None ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)
         UpperCAmelCase	    =			vocab_size
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			intermediate_size
         UpperCAmelCase	    =			hidden_dropout_prob
         UpperCAmelCase	    =			attention_probs_dropout_prob
         UpperCAmelCase	    =			max_position_embeddings
         UpperCAmelCase	    =			type_vocab_size
         UpperCAmelCase	    =			initializer_range
         UpperCAmelCase	    =			layer_norm_eps
         UpperCAmelCase	    =			position_embedding_type
         UpperCAmelCase	    =			use_cache
         UpperCAmelCase	    =			classifier_dropout





class   UpperCamelCase_ (       a_  ):



  @property
  def 		UpperCamelCase_				(      self				)			->			Mapping[str, Mapping[int, str]]:



         """simple docstring"""





         if self.task == "multiple-choice":
                UpperCAmelCase	    =			{0: """batch""", 1: """choice""", 2: """sequence"""}
         else:
                UpperCAmelCase	    =			{0: """batch""", 1: """sequence"""}
         return OrderedDict(
             [
                 ("""input_ids""", dynamic_axis),
                 ("""attention_mask""", dynamic_axis),
             ]				)
