

'''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


lowerCAmelCase__						:       List[Any]										=						logging.get_logger(__name__)
logging.set_verbosity_info()




def 	_a       (				__lowerCAmelCase		:      str   ,							__lowerCAmelCase		:      str     ):

		"""simple docstring"""

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

		snake_case__						:       int       						=  ['''key_proj''', '''value_proj''', '''query_proj''']

		snake_case__						:       Any       						=  {
		    '''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__						:       Tuple       						=  key.split('''.'''     )

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

				snake_case__						:       str       						=  False
				for attribute in attributes:
						if attribute in mapping:
								snake_case__						:       str       						=  mapping[attribute]
								if not hasattr(__lowerCAmelCase   ,							__lowerCAmelCase     ) and len(__lowerCAmelCase     ) > 0:
										snake_case__						:       Tuple       						=  attribute
						elif hasattr(__lowerCAmelCase   ,							__lowerCAmelCase     ):
								snake_case__						:       str       						=  attribute

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

								elif attribute == "key_proj":
										snake_case__						:       Union[str, Any]       						=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
										snake_case__						:       List[Any]       						=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
								elif attribute == "value_proj":
										snake_case__						:       Optional[Any]       						=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
										snake_case__						:       int       						=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
								snake_case__						:       Any       						=  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_12, "We want 512 position_embeddings."
								snake_case__						:       int       						=  nn.Parameter(old_model.embed_positions.weight[:5_12, :]     )
								snake_case__						:       Union[str, Any]       						=  True
								break

						if attribute.isdigit():
								snake_case__						:       List[str]       						=  model[int(__lowerCAmelCase     )]
								snake_case__						:       Dict       						=  old_model[int(__lowerCAmelCase     )]
						else:
								snake_case__						:       Optional[int]       						=  getattr(__lowerCAmelCase   ,							__lowerCAmelCase     )

								if old_attribute == "":
										snake_case__						:       str       						=  old_model
								else:
										if not hasattr(__lowerCAmelCase   ,							__lowerCAmelCase     ):
												raise ValueError(F"""{old_model} does not have {old_attribute}"""     )
										snake_case__						:       Tuple       						=  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__":
					lowerCAmelCase__						:       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."""
					)
					lowerCAmelCase__						:       int										=						parser.parse_args()
					convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''


from collections.abc import Generator




def 	_a       (				):

		"""simple docstring"""

		snake_case__						,       snake_case__						:       List[Any]       						=  0, 1
		while True:
				snake_case__						,       snake_case__						:       str       						=  b, a + b
				yield b




def 	_a       (				__lowerCAmelCase		:      int = 10_00     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  1
		snake_case__						:       Tuple       						=  fibonacci_generator()
		while len(str(next(__lowerCAmelCase     )     )     ) < n:
				answer += 1
		return answer + 1


if __name__ == "__main__":
					print(solution(int(str(input()).strip())))



