

"""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


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





def  _snake_case     (    UpperCAmelCase_  :					str					,   UpperCAmelCase_  :					str			):
     if "xprophetnet" in prophetnet_checkpoint_path:
          A__										=  XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCAmelCase_			)
          A__			,				A__										=  XLMProphetNetForConditionalGeneration.from_pretrained(
              UpperCAmelCase_					,   output_loading_info=UpperCAmelCase_			)
     else:
          A__										=  ProphetNetForConditionalGenerationOld.from_pretrained(UpperCAmelCase_			)
          A__			,				A__										=  ProphetNetForConditionalGeneration.from_pretrained(
              UpperCAmelCase_					,   output_loading_info=UpperCAmelCase_			)

     A__										=  ["""key_proj""", """value_proj""", """query_proj"""]

     A__										=  {
         """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"]:
          A__										=  key.split("""."""			)

          if attributes[0] == "lm_head":
               A__										=  prophet
               A__										=  prophet_old
          else:
               A__										=  prophet.prophetnet
               A__										=  prophet_old.model

          A__										=  False
          for attribute in attributes:
               if attribute in mapping:
                    A__										=  mapping[attribute]
                    if not hasattr(UpperCAmelCase_					,   UpperCAmelCase_			) and len(UpperCAmelCase_			) > 0:
                         A__										=  attribute
               elif hasattr(UpperCAmelCase_					,   UpperCAmelCase_			):
                    A__										=  attribute

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

                    elif attribute == "key_proj":
                         A__										=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]			)
                         A__										=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]			)
                    elif attribute == "value_proj":
                         A__										=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]			)
                         A__										=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]			)
                    A__										=  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."
                    A__										=  nn.Parameter(old_model.embed_positions.weight[:512, :]			)
                    A__										=  True
                    break

               if attribute.isdigit():
                    A__										=  model[int(UpperCAmelCase_			)]
                    A__										=  old_model[int(UpperCAmelCase_			)]
               else:
                    A__										=  getattr(UpperCAmelCase_					,   UpperCAmelCase_			)

                    if old_attribute == "":
                         A__										=  old_model
                    else:
                         if not hasattr(UpperCAmelCase_					,   UpperCAmelCase_			):
                              raise ValueError(F"""{old_model} does not have {old_attribute}"""			)
                         A__										=  getattr(UpperCAmelCase_					,   UpperCAmelCase_			)

          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(UpperCAmelCase_			)


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









"""simple docstring"""



def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  1
     for i in range(1					,   num + 1			):
          fact *= i
     return fact





def  _snake_case     (    UpperCAmelCase_  :					int			):
     A__										=  0
     while number > 0:
          A__										=  number % 10
          sum_of_digits += last_digit
          A__										=  number // 10  # Removing the last_digit from the given number
     return sum_of_digits





def  _snake_case     (    UpperCAmelCase_  :					int = 100			):
     A__										=  factorial(UpperCAmelCase_			)
     A__										=  split_and_add(UpperCAmelCase_			)
     return result


if __name__ == "__main__":
 print(solution(int(input('Enter the Number: ').strip())))






