



"""simple docstring"""







import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


lowercase__		:    Union[str, Any]				   =     logging.get_logger(__name__)
logging.set_verbosity_info()

def     UpperCamelCase_   (   lowerCAmelCase__				:    str   ,       lowerCAmelCase__				:    str						)						->       Tuple:

  """simple docstring"""




  if "xprophetnet" in prophetnet_checkpoint_path:
    lowerCAmelCase_       :    Optional[Any]								=			XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase__						)
    lowerCAmelCase_       :    str								=			XLMProphetNetForConditionalGeneration.from_pretrained(
        lowerCAmelCase__   ,       output_loading_info=lowerCAmelCase__						)
  else:
    lowerCAmelCase_       :    Any								=			ProphetNetForConditionalGenerationOld.from_pretrained(lowerCAmelCase__						)
    lowerCAmelCase_       :    Optional[Any]								=			ProphetNetForConditionalGeneration.from_pretrained(
        lowerCAmelCase__   ,       output_loading_info=lowerCAmelCase__						)

  lowerCAmelCase_       :    Union[str, Any]								=			['key_proj', 'value_proj', 'query_proj']

  lowerCAmelCase_       :    Tuple								=			{
      'self_attn': 'ngram_self_attn',
      'cross_attn': 'encoder_attn',
      'cross_attn_layer_norm': 'encoder_attn_layer_norm',
      'feed_forward_layer_norm': 'final_layer_norm',
      'feed_forward': '',
      'intermediate': 'fc1',
      'output': 'fc2',
      'key_proj': 'k_proj',
      'query_proj': 'q_proj',
      'value_proj': 'v_proj',
      'word_embeddings': 'embed_tokens',
      'embeddings_layer_norm': 'emb_layer_norm',
      'relative_pos_embeddings': 'relative_linear',
      'ngram_embeddings': 'ngram_input_embed',
      'position_embeddings': 'embed_positions',
  }

  for key in loading_info["missing_keys"]:
    lowerCAmelCase_       :    int								=			key.split('.'						)

    if attributes[0] == "lm_head":
      lowerCAmelCase_       :    Union[str, Any]								=			prophet
      lowerCAmelCase_       :    Optional[Any]								=			prophet_old
    else:
      lowerCAmelCase_       :    Optional[Any]								=			prophet.prophetnet
      lowerCAmelCase_       :    Union[str, Any]								=			prophet_old.model

    lowerCAmelCase_       :    Union[str, Any]								=			False
    for attribute in attributes:
      if attribute in mapping:
        lowerCAmelCase_       :    Optional[Any]								=			mapping[attribute]
        if not hasattr(lowerCAmelCase__   ,       lowerCAmelCase__						) and len(lowerCAmelCase__						) > 0:
          lowerCAmelCase_       :    Dict								=			attribute
      elif hasattr(lowerCAmelCase__   ,       lowerCAmelCase__						):
        lowerCAmelCase_       :    str								=			attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        lowerCAmelCase_       :    Optional[int]								=			old_model.weight
        logger.info(f"{attribute} is initialized."						)
        lowerCAmelCase_       :    Optional[int]								=			True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        lowerCAmelCase_       :    Dict								=			old_model.bias
        logger.info(f"{attribute} is initialized"						)
        lowerCAmelCase_       :    List[str]								=			True
        break
      elif attribute in special_keys and hasattr(lowerCAmelCase__   ,       'in_proj_weight'						):
        lowerCAmelCase_       :    Any								=			old_model.in_proj_weight.shape[0] // 3
        lowerCAmelCase_       :    int								=			getattr(lowerCAmelCase__   ,       lowerCAmelCase__						)
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          lowerCAmelCase_       :    Union[str, Any]								=			nn.Parameter(old_model.in_proj_weight[:embed_dim, :]						)
          lowerCAmelCase_       :    Optional[Any]								=			nn.Parameter(old_model.in_proj_bias[:embed_dim]						)

        elif attribute == "key_proj":
          lowerCAmelCase_       :    Tuple								=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
          lowerCAmelCase_       :    List[Any]								=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
        elif attribute == "value_proj":
          lowerCAmelCase_       :    List[str]								=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
          lowerCAmelCase_       :    Any								=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
        lowerCAmelCase_       :    List[str]								=			True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        lowerCAmelCase_       :    List[Any]								=			nn.Parameter(old_model.embed_positions.weight[:512, :]						)
        lowerCAmelCase_       :    Optional[Any]								=			True
        break

      if attribute.isdigit():
        lowerCAmelCase_       :    List[Any]								=			model[int(lowerCAmelCase__						)]
        lowerCAmelCase_       :    Optional[Any]								=			old_model[int(lowerCAmelCase__						)]
      else:
        lowerCAmelCase_       :    Optional[Any]								=			getattr(lowerCAmelCase__   ,       lowerCAmelCase__						)

        if old_attribute == "":
          lowerCAmelCase_       :    Tuple								=			old_model
        else:
          if not hasattr(lowerCAmelCase__   ,       lowerCAmelCase__						):
            raise ValueError(f"{old_model} does not have {old_attribute}"						)
          lowerCAmelCase_       :    Optional[int]								=			getattr(lowerCAmelCase__   ,       lowerCAmelCase__						)

    if not is_key_init:
      raise ValueError(f"{key} was not correctly initialized!"						)

  print(f"Saving model to {pytorch_dump_folder_path}"						)
  prophet.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:    Dict				   =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--prophetnet_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."""
     )
     lowercase__		:    str				   =     parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase__		:    List[Any]				   =     logging.getLogger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""masked_bert"""



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_0_5_2_2   ,      SCREAMING_SNAKE_CASE_    :				int=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0_7_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.02   ,      SCREAMING_SNAKE_CASE_    :				Any=1E-12   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="topK"   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="constant"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			vocab_size
        lowerCAmelCase_       :    str								=			hidden_size
        lowerCAmelCase_       :    Optional[int]								=			num_hidden_layers
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_dropout_prob
        lowerCAmelCase_       :    str								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Any								=			max_position_embeddings
        lowerCAmelCase_       :    Dict								=			type_vocab_size
        lowerCAmelCase_       :    Tuple								=			initializer_range
        lowerCAmelCase_       :    List[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			pruning_method
        lowerCAmelCase_       :    Optional[Any]								=			mask_init
        lowerCAmelCase_       :    int								=			mask_scale





