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  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       List[str] ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       Tuple=None ,     __UpperCamelCase    :       List[Any]=None	)							->					Union[str, Any]:







  """simple docstring"""





  if "." in tensor_name:
    SCREAMING_SNAKE_CASE__										=      tensor_name.split("""."""	)
    for split in splits[:-1]:
      SCREAMING_SNAKE_CASE__										=      getattr(__UpperCamelCase ,     __UpperCamelCase	)
      if new_module is None:
        raise ValueError(f"""{module} has no attribute {split}."""	)
      SCREAMING_SNAKE_CASE__										=      new_module
    SCREAMING_SNAKE_CASE__										=      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__										=      tensor_name in module._buffers
  SCREAMING_SNAKE_CASE__										=      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}."""	)

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

  if is_abit or is_abit:
    SCREAMING_SNAKE_CASE__										=      module._parameters[tensor_name]
    if param.device.type != "cuda":
      if value is None:
        SCREAMING_SNAKE_CASE__										=      old_value.to(__UpperCamelCase	)
      elif isinstance(__UpperCamelCase ,     torch.Tensor	):
        SCREAMING_SNAKE_CASE__										=      value.to("""cpu"""	)
        if value.dtype == torch.inta:
          SCREAMING_SNAKE_CASE__										=      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__										=      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:
        SCREAMING_SNAKE_CASE__										=      new_value.T

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

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

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

    if is_buffer:
      SCREAMING_SNAKE_CASE__										=      new_value
    else:
      SCREAMING_SNAKE_CASE__										=      nn.Parameter(__UpperCamelCase ,     requires_grad=old_value.requires_grad	)
      SCREAMING_SNAKE_CASE__										=      new_value



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       str=None ,     __UpperCamelCase    :       Optional[Any]=None ,     __UpperCamelCase    :       List[str]=None ,     __UpperCamelCase    :       Optional[Any]=False	)							->					str:







  """simple docstring"""





  for name, module in model.named_children():
    if current_key_name is None:
      SCREAMING_SNAKE_CASE__										=      []
    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	):
            SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      module.weight.shape
          else:
            SCREAMING_SNAKE_CASE__										=      module.in_features
            SCREAMING_SNAKE_CASE__										=      module.out_features

          if quantization_config.quantization_method() == "llm_int8":
            SCREAMING_SNAKE_CASE__										=      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 ,     )
            SCREAMING_SNAKE_CASE__										=      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__										=      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 ,     )
              SCREAMING_SNAKE_CASE__										=      True
                    # Store the module class in case we need to transpose the weight later
          SCREAMING_SNAKE_CASE__										=      type(__UpperCamelCase	)
          # Force requires grad to False to avoid unexpected errors
          model._modules[name].requires_grad_(__UpperCamelCase	)
    if len(list(module.children()	)	) > 0:
      SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      _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    :       str ,     __UpperCamelCase    :       int=None ,     __UpperCamelCase    :       List[str]=None ,     __UpperCamelCase    :       str=None	)							->					int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      ["""lm_head"""] if modules_to_not_convert is None else modules_to_not_convert
  SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      _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    :       Optional[int] ,     **__UpperCamelCase    :       Dict	)							->					Union[str, Any]:







  """simple docstring"""





  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    :       int ,     **__UpperCamelCase    :       Union[str, Any]	)							->					str:







  """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""" ,     __UpperCamelCase ,     )
  return set_module_quantized_tensor_to_device(*__UpperCamelCase ,     **__UpperCamelCase	)






def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int	)							->					Dict:







  """simple docstring"""





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

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

  # Check if it is a base model
  SCREAMING_SNAKE_CASE__										=      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
  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(__UpperCamelCase	) - set(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      list(set(__UpperCamelCase	)	) + list(__UpperCamelCase	)

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

  return filtered_module_names

import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
   raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
   raise Exception('''requires fairseq < v2''')

logging.set_verbosity_info()
__lowerCamelCase			: Dict							   =		logging.get_logger(__name__)

__lowerCamelCase			: Dict							   =		'''Hello, World!'''
__lowerCamelCase			: Optional[Any]							   =		'''en_XX'''



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       str ,     __UpperCamelCase    :       str ,     __UpperCamelCase    :       bool	)							->					Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      Path("""data_bin"""	)
  SCREAMING_SNAKE_CASE__										=      FairseqXmodModel.from_pretrained(
      model_name_or_path=str(Path(__UpperCamelCase	).parent	) ,     checkpoint_file=Path(__UpperCamelCase	).name ,     _name="""xmod_base""" ,     arch="""xmod_base""" ,     task="""multilingual_masked_lm""" ,     data_name_or_path=str(__UpperCamelCase	) ,     bpe="""sentencepiece""" ,     sentencepiece_model=str(Path(__UpperCamelCase	).parent / """sentencepiece.bpe.model"""	) ,     src_dict=str(data_dir / """dict.txt"""	) ,     )
  xmod.eval()  # disable dropout
  print(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.sentence_encoder
  SCREAMING_SNAKE_CASE__										=      XmodConfig(
      vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,     hidden_size=xmod.cfg.model.encoder_embed_dim ,     num_hidden_layers=xmod.cfg.model.encoder_layers ,     num_attention_heads=xmod.cfg.model.encoder_attention_heads ,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,     max_position_embeddings=5_14 ,     type_vocab_size=1 ,     layer_norm_eps=1E-5 ,     pre_norm=xmod.cfg.model.encoder_normalize_before ,     adapter_reduction_factor=getattr(xmod.cfg.model ,     """bottleneck""" ,     2	) ,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,     ln_before_adapter=xmod.cfg.model.ln_before_adapter ,     languages=xmod.cfg.model.languages ,     )
  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our X-MOD config:""" ,     __UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      XmodForSequenceClassification(__UpperCamelCase	) if classification_head else XmodForMaskedLM(__UpperCamelCase	)
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.embed_tokens.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.embed_positions.weight
  SCREAMING_SNAKE_CASE__										=      torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.weight
  SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layernorm_embedding.bias

  for i in range(config.num_hidden_layers	):
    # Encoder: start of layer
    SCREAMING_SNAKE_CASE__										=      model.roberta.encoder.layer[i]
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layers[i]

    # self attention
    SCREAMING_SNAKE_CASE__										=      layer.attention.self
    if not (
        xmod_layer.self_attn.k_proj.weight.data.shape
        == xmod_layer.self_attn.q_proj.weight.data.shape
        == xmod_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)	)
    ):
      raise AssertionError("""Dimensions of self-attention weights do not match."""	)

    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.q_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.k_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.v_proj.bias

    # self-attention output
    SCREAMING_SNAKE_CASE__										=      layer.attention.output
    if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
      raise AssertionError("""Dimensions of self-attention output weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn.out_proj.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.self_attn_layer_norm.bias

    # intermediate
    SCREAMING_SNAKE_CASE__										=      layer.intermediate
    if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of intermediate weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias

    # output
    SCREAMING_SNAKE_CASE__										=      layer.output
    if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
      raise AssertionError("""Dimensions of feed-forward weights do not match."""	)
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.fca.bias
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_layer.final_layer_norm.bias
    if bert_output.adapter_layer_norm is not None:
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.weight
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_layer_norm.bias

    if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
      raise AssertionError("""Lists of language adapters do not match."""	)
    for lang_code, adapter in xmod_layer.adapter_modules.items():
      SCREAMING_SNAKE_CASE__										=      bert_output.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      xmod_layer.adapter_modules[lang_code]
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.weight
      SCREAMING_SNAKE_CASE__										=      from_adapter.fca.bias

    # end of layer

  if xmod_sent_encoder.layer_norm is not None:
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod_sent_encoder.layer_norm.bias

  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.dense.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.layer_norm.bias
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.weight
    SCREAMING_SNAKE_CASE__										=      xmod.model.encoder.lm_head.bias

  # Let's check that we get the same results.
  SCREAMING_SNAKE_CASE__										=      xmod.encode(__UpperCamelCase	).unsqueeze(0	)  # batch of size 1
  model.roberta.set_default_language(__UpperCamelCase	)

  SCREAMING_SNAKE_CASE__										=      model(__UpperCamelCase	)[0]
  if classification_head:
    SCREAMING_SNAKE_CASE__										=      xmod.model.classification_heads["""mnli"""](xmod.extract_features(__UpperCamelCase	)	)
  else:
    SCREAMING_SNAKE_CASE__										=      xmod.model(__UpperCamelCase ,     lang_id=[SAMPLE_LANGUAGE]	)[0]
  print(our_output.shape ,     their_output.shape	)
  SCREAMING_SNAKE_CASE__										=      torch.max(torch.abs(our_output - their_output	)	).item()
  print(f"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
  SCREAMING_SNAKE_CASE__										=      torch.allclose(__UpperCamelCase ,     __UpperCamelCase ,     atol=1E-3	)
  print("""Do both models output the same tensors?""" ,     """🔥""" if success else """💩"""	)
  if not success:
    raise Exception("""Something went wRoNg"""	)

  Path(__UpperCamelCase	).mkdir(parents=__UpperCamelCase ,     exist_ok=__UpperCamelCase	)
  print(f"""Saving model to {pytorch_dump_folder_path}"""	)
  model.save_pretrained(__UpperCamelCase	)


if __name__ == "__main__":
   __lowerCamelCase			: Dict							   =		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
   )
   __lowerCamelCase			: str							   =		parser.parse_args()
   convert_xmod_checkpoint_to_pytorch(
       args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )
