
"""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


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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase						):
						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__":
			SCREAMING_SNAKE_CASE_					     =	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.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""
import os

def  lowercase     ():
						with open(os.path.dirname(_lowerCAmelCase						) + """/grid.txt"""						) as f:
												__lowerCAmelCase     =  []  # noqa: E741
												for _ in range(20						):
																		l.append([int(_lowerCAmelCase						) for x in f.readline().split()]						)

												__lowerCAmelCase     =  0

												# right
												for i in range(20						):
																		for j in range(17						):
																								__lowerCAmelCase     =  l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
																								if temp > maximum:
																														__lowerCAmelCase     =  temp

        # down
												for i in range(17						):
																		for j in range(20						):
																								__lowerCAmelCase     =  l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
																								if temp > maximum:
																														__lowerCAmelCase     =  temp

        # diagonal 1
												for i in range(17						):
																		for j in range(17						):
																								__lowerCAmelCase     =  l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
																								if temp > maximum:
																														__lowerCAmelCase     =  temp

        # diagonal 2
												for i in range(17						):
																		for j in range(3 ,    20						):
																								__lowerCAmelCase     =  l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
																								if temp > maximum:
																														__lowerCAmelCase     =  temp
												return maximum


if __name__ == "__main__":
			print(solution())
