

"""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__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Dict:
		if "xprophetnet" in prophetnet_checkpoint_path:
				__lowerCAmelCase:       Optional[int]          =			XLMProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase      , __lowerCAmelCase:       Optional[Any]          =			XLMProphetNetForConditionalGeneration.from_pretrained(
				    __SCREAMING_SNAKE_CASE					, output_loading_info=__SCREAMING_SNAKE_CASE )
		else:
				__lowerCAmelCase:       Optional[Any]          =			ProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE )
				__lowerCAmelCase      , __lowerCAmelCase:       Any          =			ProphetNetForConditionalGeneration.from_pretrained(
				    __SCREAMING_SNAKE_CASE					, output_loading_info=__SCREAMING_SNAKE_CASE )

		__lowerCAmelCase:       str          =			["key_proj", "value_proj", "query_proj"]

		__lowerCAmelCase:       int          =			{
		    "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:       Dict          =			key.split("." )

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

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

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

								elif attribute == "key_proj":
										__lowerCAmelCase:       Any          =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
										__lowerCAmelCase:       List[Any]          =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
								elif attribute == "value_proj":
										__lowerCAmelCase:       Tuple          =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
										__lowerCAmelCase:       Optional[int]          =			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] == 5_1_2, "We want 512 position_embeddings."
								__lowerCAmelCase:       List[Any]          =			nn.Parameter(old_model.embed_positions.weight[:5_1_2, :] )
								__lowerCAmelCase:       Optional[Any]          =			True
								break

						if attribute.isdigit():
								__lowerCAmelCase:       List[str]          =			model[int(__SCREAMING_SNAKE_CASE )]
								__lowerCAmelCase:       Optional[Any]          =			old_model[int(__SCREAMING_SNAKE_CASE )]
						else:
								__lowerCAmelCase:       int          =			getattr(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )

								if old_attribute == "":
										__lowerCAmelCase:       int          =			old_model
								else:
										if not hasattr(__SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE ):
												raise ValueError(F"{old_model} does not have {old_attribute}" )
										__lowerCAmelCase:       Any          =			getattr(__SCREAMING_SNAKE_CASE					, __SCREAMING_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(__SCREAMING_SNAKE_CASE )


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"""


__A						   =			{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	list[str]:
		__lowerCAmelCase:       Tuple          =			set()
		# keep track of all the paths to be checked
		__lowerCAmelCase:       Optional[Any]          =			[[start]]

		# return path if start is goal
		if start == goal:
				return [start]

		# keeps looping until all possible paths have been checked
		while queue:
				# pop the first path from the queue
				__lowerCAmelCase:       str          =			queue.pop(0 )
				# get the last node from the path
				__lowerCAmelCase:       List[Any]          =			path[-1]
				if node not in explored:
						__lowerCAmelCase:       str          =			graph[node]
						# go through all neighbour nodes, construct a new path and
						# push it into the queue
						for neighbour in neighbours:
								__lowerCAmelCase:       Optional[int]          =			list(__SCREAMING_SNAKE_CASE )
								new_path.append(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								# return path if neighbour is goal
								if neighbour == goal:
										return new_path

            # mark node as explored
						explored.add(__SCREAMING_SNAKE_CASE )

    # in case there's no path between the 2 nodes
		return []






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		if not graph or start not in graph or target not in graph:
				return -1
		if start == target:
				return 0
		__lowerCAmelCase:       Dict          =			[start]
		__lowerCAmelCase:       Any          =			set(__SCREAMING_SNAKE_CASE )
		# Keep tab on distances from `start` node.
		__lowerCAmelCase:       int          =			{start: 0, target: -1}
		while queue:
				__lowerCAmelCase:       Optional[Any]          =			queue.pop(0 )
				if node == target:
						__lowerCAmelCase:       Dict          =			(
						    dist[node] if dist[target] == -1 else min(dist[target]					, dist[node] )
						)
				for adjacent in graph[node]:
						if adjacent not in visited:
								visited.add(__SCREAMING_SNAKE_CASE )
								queue.append(__SCREAMING_SNAKE_CASE )
								__lowerCAmelCase:       List[str]          =			dist[node] + 1
		return dist[target]


if __name__ == "__main__":
			print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
			print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4


