





"""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		:				List[str]  			=    logging.get_logger(__name__)
logging.set_verbosity_info()






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					Union[str, Any]      )				->List[Any]:






	'''simple docstring'''






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

	a   :	int			=       ["key_proj", "value_proj", "query_proj"]

	a   :	str			=       {
	    "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   :	Union[str, Any]			=       key.split("."      )

		if attributes[0] == "lm_head":
			a   :	Tuple			=       prophet
			a   :	Tuple			=       prophet_old
		else:
			a   :	Tuple			=       prophet.prophetnet
			a   :	List[str]			=       prophet_old.model

		a   :	int			=       False
		for attribute in attributes:
			if attribute in mapping:
				a   :	int			=       mapping[attribute]
				if not hasattr(lowerCamelCase__					, lowerCamelCase__      ) and len(lowerCamelCase__      ) > 0:
					a   :	Dict			=       attribute
			elif hasattr(lowerCamelCase__					, lowerCamelCase__      ):
				a   :	Optional[Any]			=       attribute

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

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

			if attribute.isdigit():
				a   :	str			=       model[int(lowerCamelCase__      )]
				a   :	Union[str, Any]			=       old_model[int(lowerCamelCase__      )]
			else:
				a   :	int			=       getattr(lowerCamelCase__					, lowerCamelCase__      )

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









"""simple docstring"""




from ..utils import DummyObject, requires_backends


class  __UpperCamelCase       (		metaclass=a__		):
							lowerCamelCase     :      Optional[Any]         =["""transformers""", """torch""", """note_seq"""]






							def __init__(     self   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								requires_backends(self   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Any:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )






							@classmethod
							def 					__a     (     cls   ,      *lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				int:
								requires_backends(cls   ,      ["transformers", "torch", "note_seq"]   )


