


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


a_ :Tuple  							=			logging.get_logger(__name__)
logging.set_verbosity_info()




def    a   (							A__      ,    A__							)	-> List[str]:




      '''simple docstring'''

      if "xprophetnet" in prophetnet_checkpoint_path:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		XLMProphetNetForConditionalGenerationOld.from_pretrained(A__							)
            SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		XLMProphetNetForConditionalGeneration.from_pretrained(
                A__      ,    output_loading_info=A__							)
      else:
            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		ProphetNetForConditionalGenerationOld.from_pretrained(A__							)
            SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Dict						     =		ProphetNetForConditionalGeneration.from_pretrained(
                A__      ,    output_loading_info=A__							)

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

      SCREAMING_SNAKE_CASE__       :  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"]:
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		key.split('''.'''							)

            if attributes[0] == "lm_head":
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		prophet
                  SCREAMING_SNAKE_CASE__       :  Dict						     =		prophet_old
            else:
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		prophet.prophetnet
                  SCREAMING_SNAKE_CASE__       :  Any						     =		prophet_old.model

            SCREAMING_SNAKE_CASE__       :  Tuple						     =		False
            for attribute in attributes:
                  if attribute in mapping:
                        SCREAMING_SNAKE_CASE__       :  List[str]						     =		mapping[attribute]
                        if not hasattr(A__      ,    A__							) and len(A__							) > 0:
                              SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		attribute
                  elif hasattr(A__      ,    A__							):
                        SCREAMING_SNAKE_CASE__       :  Any						     =		attribute

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

                        elif attribute == "key_proj":
                              SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]							)
                              SCREAMING_SNAKE_CASE__       :  List[Any]						     =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]							)
                        elif attribute == "value_proj":
                              SCREAMING_SNAKE_CASE__       :  List[Any]						     =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]							)
                              SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]							)
                        SCREAMING_SNAKE_CASE__       :  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] == 5_1_2, "We want 512 position_embeddings."
                        SCREAMING_SNAKE_CASE__       :  List[Any]						     =		nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]							)
                        SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		True
                        break

                  if attribute.isdigit():
                        SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		model[int(A__							)]
                        SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		old_model[int(A__							)]
                  else:
                        SCREAMING_SNAKE_CASE__       :  List[Any]						     =		getattr(A__      ,    A__							)

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

            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(A__							)


if __name__ == "__main__":
    a_ :Union[str, Any]  							=			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.'
    )
    a_ :int  							=			parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)










import numpy as np




def       a_ (       SCREAMING_SNAKE_CASE__   : np.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       return np.where(vector > 0						,     SCREAMING_SNAKE_CASE__						,     (alpha * (np.exp(SCREAMING_SNAKE_CASE__	) - 1))	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


