




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


lowerCamelCase	:	List[str]				=   logging.get_logger(__name__)
logging.set_verbosity_info()




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:
   if "xprophetnet" in prophetnet_checkpoint_path:
      snake_case				:    Dict           =	XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase					)
      snake_case     , snake_case				:    List[str]           =	XLMProphetNetForConditionalGeneration.from_pretrained(
          lowercase						,output_loading_info=lowercase					)
   else:
      snake_case				:    Optional[int]           =	ProphetNetForConditionalGenerationOld.from_pretrained(lowercase					)
      snake_case     , snake_case				:    int           =	ProphetNetForConditionalGeneration.from_pretrained(
          lowercase						,output_loading_info=lowercase					)

   snake_case				:    List[Any]           =	["""key_proj""", """value_proj""", """query_proj"""]

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

      if attributes[0] == "lm_head":
         snake_case				:    Tuple           =	prophet
         snake_case				:    Optional[int]           =	prophet_old
      else:
         snake_case				:    int           =	prophet.prophetnet
         snake_case				:    Optional[Any]           =	prophet_old.model

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

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

            elif attribute == "key_proj":
               snake_case				:    Tuple           =	nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
               snake_case				:    int           =	nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
            elif attribute == "value_proj":
               snake_case				:    Optional[int]           =	nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
               snake_case				:    str           =	nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
            snake_case				:    Optional[int]           =	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."
            snake_case				:    Optional[int]           =	nn.Parameter(old_model.embed_positions.weight[:512, :]					)
            snake_case				:    Any           =	True
            break

         if attribute.isdigit():
            snake_case				:    int           =	model[int(lowercase					)]
            snake_case				:    Optional[Any]           =	old_model[int(lowercase					)]
         else:
            snake_case				:    Any           =	getattr(lowercase						,lowercase					)

            if old_attribute == "":
               snake_case				:    List[Any]           =	old_model
            else:
               if not hasattr(lowercase						,lowercase					):
                  raise ValueError(f"""{old_model} does not have {old_attribute}"""					)
               snake_case				:    Tuple           =	getattr(lowercase						,lowercase					)

      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(lowercase					)


if __name__ == "__main__":
       lowerCamelCase	:	Optional[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.'
       )
       lowerCamelCase	:	Optional[Any]				=   parser.parse_args()
       convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)











import functools




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:

   # Validation
   if not isinstance(lowercase						,lowercase					) or not all(isinstance(lowercase						,lowercase					) for day in days					):
      raise ValueError("""The parameter days should be a list of integers"""					)

   if len(lowercase					) != 3 or not all(isinstance(lowercase						,lowercase					) for cost in costs					):
      raise ValueError("""The parameter costs should be a list of three integers"""					)

   if len(lowercase					) == 0:
      return 0

   if min(lowercase					) <= 0:
      raise ValueError("""All days elements should be greater than 0"""					)

   if max(lowercase					) >= 366:
      raise ValueError("""All days elements should be less than 366"""					)

   snake_case				:    List[str]           =	set(lowercase					)

   @functools.cache
   def dynamic_programming(lowercase					) -> int:
      if index > 365:
         return 0

      if index not in days_set:
         return dynamic_programming(index + 1					)

      return min(
          costs[0] + dynamic_programming(index + 1					)						,costs[1] + dynamic_programming(index + 7					)						,costs[2] + dynamic_programming(index + 30					)						,)

   return dynamic_programming(1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





