


"""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


a_       							=    logging.get_logger(__name__)
logging.set_verbosity_info()






def      a__ (				__lowercase     ,			__lowercase   )	->	Dict:
				if "xprophetnet" in prophetnet_checkpoint_path:
								_A					  =			XLMProphetNetForConditionalGenerationOld.from_pretrained(__lowercase   )
								_A  ,		_A					  =			XLMProphetNetForConditionalGeneration.from_pretrained(
								    __lowercase     ,			output_loading_info=__lowercase   )
				else:
								_A					  =			ProphetNetForConditionalGenerationOld.from_pretrained(__lowercase   )
								_A  ,		_A					  =			ProphetNetForConditionalGeneration.from_pretrained(
								    __lowercase     ,			output_loading_info=__lowercase   )

				_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(__lowercase     ,			__lowercase   ) and len(__lowercase   ) > 0:
																				_A					  =			attribute
												elif hasattr(__lowercase     ,			__lowercase   ):
																_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(__lowercase     ,			"in_proj_weight"   ):
																_A					  =			old_model.in_proj_weight.shape[0] // 3
																_A					  =			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":
																				_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(__lowercase   )]
																_A					  =			old_model[int(__lowercase   )]
												else:
																_A					  =			getattr(__lowercase     ,			__lowercase   )

																if old_attribute == "":
																				_A					  =			old_model
																else:
																				if not hasattr(__lowercase     ,			__lowercase   ):
																								raise ValueError(f"""{old_model} does not have {old_attribute}"""   )
																				_A					  =			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__":
		a_       							=    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."
		)
		a_       							=    parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""


from __future__ import annotations






def      a__ (				__lowercase     ,			__lowercase   )	->	float:
				_A					  =			sorted(numsa + numsa   )
				_A  ,		_A					  =			divmod(len(__lowercase   )     ,			2   )
				if mod == 1:
								return all_numbers[div]
				else:
								return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		a_       							=    [float(x) for x in input("Enter the elements of first array: ").split()]
		a_       							=    [float(x) for x in input("Enter the elements of second array: ").split()]
		print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')