"""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    :							Any													=						logging.get_logger(__name__)
logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		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__":
			__lowerCAmelCase    :							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."
			)
			__lowerCAmelCase    :							int													=						parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		assert isinstance(lowerCamelCase__			,  lowerCamelCase__     ), f"""The input value of [n={number}] is not an integer"""

		if number == 1:
				return 2
		elif number < 1:
				lowerCAmelCase__        =   f"""The input value of [n={number}] has to be > 0"""
				raise ValueError(lowerCamelCase__     )
		else:
				lowerCAmelCase__        =   sylvester(number - 1     )
				lowerCAmelCase__        =   num - 1
				lowerCAmelCase__        =   num
				return lower * upper + 1


if __name__ == "__main__":
			print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")




