
"""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__   					=		logging.get_logger(__name__)
logging.set_verbosity_info()
def     __lowerCAmelCase				(_UpperCamelCase       ,  _UpperCamelCase		):
	if "xprophetnet" in prophetnet_checkpoint_path:
		__lowerCAmelCase			:						int									=      XLMProphetNetForConditionalGenerationOld.from_pretrained(snake_case__		)
		__lowerCAmelCase			,   __lowerCAmelCase			:						Optional[Any]									=      XLMProphetNetForConditionalGeneration.from_pretrained(
		    snake_case__       ,  output_loading_info=snake_case__		)
	else:
		__lowerCAmelCase			:						str									=      ProphetNetForConditionalGenerationOld.from_pretrained(snake_case__		)
		__lowerCAmelCase			,   __lowerCAmelCase			:						int									=      ProphetNetForConditionalGeneration.from_pretrained(
		    snake_case__       ,  output_loading_info=snake_case__		)

	__lowerCAmelCase			:						Optional[Any]									=      ['key_proj', 'value_proj', 'query_proj']

	__lowerCAmelCase			:						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"]:
		__lowerCAmelCase			:						Any									=      key.split('.'		)

		if attributes[0] == "lm_head":
			__lowerCAmelCase			:						Optional[int]									=      prophet
			__lowerCAmelCase			:						Union[str, Any]									=      prophet_old
		else:
			__lowerCAmelCase			:						Any									=      prophet.prophetnet
			__lowerCAmelCase			:						Optional[Any]									=      prophet_old.model

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

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

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

			if attribute.isdigit():
				__lowerCAmelCase			:						Tuple									=      model[int(snake_case__		)]
				__lowerCAmelCase			:						Any									=      old_model[int(snake_case__		)]
			else:
				__lowerCAmelCase			:						Any									=      getattr(snake_case__       ,  snake_case__		)

				if old_attribute == "":
					__lowerCAmelCase			:						Tuple									=      old_model
				else:
					if not hasattr(snake_case__       ,  snake_case__		):
						raise ValueError(F"{old_model} does not have {old_attribute}"		)
					__lowerCAmelCase			:						Tuple									=      getattr(snake_case__       ,  snake_case__		)

		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(snake_case__		)


if __name__ == "__main__":
							lowerCamelCase__   					=		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__   					=		parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

def 				a						(							snake_case__:					int = 100					):


				'''simple docstring'''
				lowercase_       =					(n * (n + 1) // 2) ** 2
				lowercase_       =					n * (n + 1) * (2 * n + 1) // 6
				return sum_cubes - sum_squares


if __name__ == "__main__":
						print(f"{solution() = }")




