





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





def  __magic_name__  (				lowercase				,       lowercase				)				-> List[Any]:

							"""simple docstring"""



							if "xprophetnet" in prophetnet_checkpoint_path:
														lowercase_	:   Optional[int]  				=     XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase				)
														lowercase_				,		lowercase_	:   List[Any]  				=     XLMProphetNetForConditionalGeneration.from_pretrained(
														    lowercase				,       output_loading_info=lowercase				)
							else:
														lowercase_	:   Optional[int]  				=     ProphetNetForConditionalGenerationOld.from_pretrained(lowercase				)
														lowercase_				,		lowercase_	:   Any  				=     ProphetNetForConditionalGeneration.from_pretrained(
														    lowercase				,       output_loading_info=lowercase				)

							lowercase_	:   List[str]  				=     ["""key_proj""", """value_proj""", """query_proj"""]

							lowercase_	:   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"]:
														lowercase_	:   Optional[Any]  				=     key.split("""."""				)

														if attributes[0] == "lm_head":
																					lowercase_	:   Union[str, Any]  				=     prophet
																					lowercase_	:   Union[str, Any]  				=     prophet_old
														else:
																					lowercase_	:   Dict  				=     prophet.prophetnet
																					lowercase_	:   List[Any]  				=     prophet_old.model

														lowercase_	:   List[str]  				=     False
														for attribute in attributes:
																					if attribute in mapping:
																												lowercase_	:   int  				=     mapping[attribute]
																												if not hasattr(lowercase				,       lowercase				) and len(lowercase				) > 0:
																																			lowercase_	:   Union[str, Any]  				=     attribute
																					elif hasattr(lowercase				,       lowercase				):
																												lowercase_	:   Optional[Any]  				=     attribute

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

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

																					if attribute.isdigit():
																												lowercase_	:   int  				=     model[int(lowercase				)]
																												lowercase_	:   Optional[int]  				=     old_model[int(lowercase				)]
																					else:
																												lowercase_	:   List[str]  				=     getattr(lowercase				,       lowercase				)

																												if old_attribute == "":
																																			lowercase_	:   List[str]  				=     old_model
																												else:
																																			if not hasattr(lowercase				,       lowercase				):
																																										raise ValueError(f"""{old_model} does not have {old_attribute}"""				)
																																			lowercase_	:   List[str]  				=     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__":
							UpperCAmelCase_							=				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_							=				parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






UpperCAmelCase_							=				8.31_44_62  # Unit - J mol-1 K-1





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or volume < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				)				-> float:

							"""simple docstring"""



							if moles < 0 or kelvin < 0 or pressure < 0:
														raise ValueError("""Invalid inputs. Enter positive value."""				)
							return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure


if __name__ == "__main__":
							from doctest import testmod

							testmod()