"""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     :				str    				=    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__ ,       lowerCAmelCase__   = XLMProphetNetForConditionalGeneration.from_pretrained(
				    lowerCamelCase__      ,	output_loading_info=lowerCamelCase__  )
		else:
				lowerCAmelCase__   = ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__  )
				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     :				Tuple    				=    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     :				List[Any]    				=    parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""




from __future__ import annotations

from typing import Any

class    a_   :




					def __init__(						self    :  Union[str, Any]	,    snake_case__    :  int = 6  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							self.create_linked_list(snake_case__  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  int  ):
							lowerCAmelCase__   = Node()
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							lowerCAmelCase__   = current_node
							for _ in range(1	,    snake_case__  ):
									lowerCAmelCase__   = Node()
									lowerCAmelCase__   = current_node
									lowerCAmelCase__   = previous_node
									lowerCAmelCase__   = current_node
							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = previous_node




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							return (
							    self.front == self.rear
							    and self.front is not None
							    and self.front.data is None
							)




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							return self.front.data if self.front else None




					def     _SCREAMING_SNAKE_CASE    (						self    :  Dict	,    snake_case__    :  Any  ):
							if self.rear is None:
									return

							self.check_is_full()
							if not self.is_empty():
									lowerCAmelCase__   = self.rear.next
							if self.rear:
									lowerCAmelCase__   = data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Any  ):
							self.check_can_perform_operation()
							if self.rear is None or self.front is None:
									return None
							if self.front == self.rear:
									lowerCAmelCase__   = self.front.data
									lowerCAmelCase__   = None
									return data

							lowerCAmelCase__   = self.front
							lowerCAmelCase__   = old_front.next
							lowerCAmelCase__   = old_front.data
							lowerCAmelCase__   = None
							return data




					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]  ):
							if self.is_empty():
									raise Exception("""Empty Queue"""  )




					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[Any]  ):
							if self.rear and self.rear.next == self.front:
									raise Exception("""Full Queue"""  )

class    a_   :




					def __init__(						self    :  Union[str, Any]  ):
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None
							lowerCAmelCase__   = None


if __name__ == "__main__":
			import doctest

			doctest.testmod()




