






'''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


UpperCamelCase    :      int  			=		logging.get_logger(__name__)
logging.set_verbosity_info()
def 						A__			(				__lowerCAmelCase		:			Tuple   ,    __lowerCAmelCase		:			Optional[int]						):
						if "xprophetnet" in prophetnet_checkpoint_path:
												lowerCamelCase__         =  XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_						)
												lowerCamelCase__         =  XLMProphetNetForConditionalGeneration.from_pretrained(
												    lowerCamelCase_   ,    output_loading_info=lowerCamelCase_						)
						else:
												lowerCamelCase__         =  ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase_						)
												lowerCamelCase__         =  ProphetNetForConditionalGeneration.from_pretrained(
												    lowerCamelCase_   ,    output_loading_info=lowerCamelCase_						)

						lowerCamelCase__         =  ['key_proj', 'value_proj', 'query_proj']

						lowerCamelCase__         =  {
						    '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__         =  key.split("""."""						)

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

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

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

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

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

																								if old_attribute == "":
																														lowerCamelCase__         =  old_model
																								else:
																														if not hasattr(lowerCamelCase_   ,    lowerCamelCase_						):
																																				raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
																														lowerCamelCase__         =  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__":
							UpperCamelCase    :      Optional[int]  			=		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.'
							)
							UpperCamelCase    :      Any  			=		parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''







from math import factorial

UpperCamelCase    :      dict[str, int]  			=		{str(digit): factorial(digit) for digit in range(10)}
def 						A__			(				__lowerCAmelCase		:			int						):
						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise TypeError("""Parameter number must be int"""						)

						if number < 0:
												raise ValueError("""Parameter number must be greater than or equal to 0"""						)

						# Converts number in string to iterate on its digits and adds its factorial.
						return sum(DIGIT_FACTORIAL[digit] for digit in str(__lowerCAmelCase						)						)
def 						A__			(				__lowerCAmelCase		:			int = 60   ,    __lowerCAmelCase		:			int = 100_0000						):

						if not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						) or not isinstance(__lowerCAmelCase   ,    __lowerCAmelCase						):
												raise TypeError("""Parameters chain_length and number_limit must be int"""						)

						if chain_length <= 0 or number_limit <= 0:
												raise ValueError(
												    """Parameters chain_length and number_limit must be greater than 0"""						)

						# the counter for the chains with the exact desired length
						lowerCamelCase__         =  0
						# the cached sizes of the previous chains
						lowerCamelCase__         =  {}

						for start_chain_element in range(1   ,    __lowerCAmelCase						):
												# The temporary set will contain the elements of the chain
												lowerCamelCase__         =  set()
												lowerCamelCase__         =  0

												# Stop computing the chain when you find a cached size, a repeating item or the
												# length is greater then the desired one.
												lowerCamelCase__         =  start_chain_element
												while (
												    chain_element not in chain_sets_lengths
												    and chain_element not in chain_set
												    and chain_set_length <= chain_length
												):
																		chain_set.add(__lowerCAmelCase						)
																		chain_set_length += 1
																		lowerCamelCase__         =  digit_factorial_sum(__lowerCAmelCase						)

												if chain_element in chain_sets_lengths:
																		chain_set_length += chain_sets_lengths[chain_element]

												lowerCamelCase__         =  chain_set_length

												# If chain contains the exact amount of elements increase the counter
												if chain_set_length == chain_length:
																		chains_counter += 1

						return chains_counter


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(F'{solution()}')

