
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


_snake_case      :							Optional[Any]     	=    logging.get_logger(__name__)
logging.set_verbosity_info()

def  __snake_case							(	__magic_name__		,    __magic_name__						):







				'''simple docstring'''


				if "xprophetnet" in prophetnet_checkpoint_path:
								lowercase         =      XLMProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__						)
								lowercase     ,	lowercase         =      XLMProphetNetForConditionalGeneration.from_pretrained(
								    __magic_name__		,    output_loading_info=__magic_name__						)
				else:
								lowercase         =      ProphetNetForConditionalGenerationOld.from_pretrained(__magic_name__						)
								lowercase     ,	lowercase         =      ProphetNetForConditionalGeneration.from_pretrained(
								    __magic_name__		,    output_loading_info=__magic_name__						)

				lowercase         =      ["key_proj", "value_proj", "query_proj"]

				lowercase         =      {
				    "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"]:
								lowercase         =      key.split("."						)

								if attributes[0] == "lm_head":
												lowercase         =      prophet
												lowercase         =      prophet_old
								else:
												lowercase         =      prophet.prophetnet
												lowercase         =      prophet_old.model

								lowercase         =      False
								for attribute in attributes:
												if attribute in mapping:
																lowercase         =      mapping[attribute]
																if not hasattr(__magic_name__		,    __magic_name__						) and len(__magic_name__						) > 0:
																				lowercase         =      attribute
												elif hasattr(__magic_name__		,    __magic_name__						):
																lowercase         =      attribute

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

																elif attribute == "key_proj":
																				lowercase         =      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
																				lowercase         =      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
																elif attribute == "value_proj":
																				lowercase         =      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
																				lowercase         =      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
																lowercase         =      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."
																lowercase         =      nn.Parameter(old_model.embed_positions.weight[:512, :]						)
																lowercase         =      True
																break

												if attribute.isdigit():
																lowercase         =      model[int(__magic_name__						)]
																lowercase         =      old_model[int(__magic_name__						)]
												else:
																lowercase         =      getattr(__magic_name__		,    __magic_name__						)

																if old_attribute == "":
																				lowercase         =      old_model
																else:
																				if not hasattr(__magic_name__		,    __magic_name__						):
																								raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
																				lowercase         =      getattr(__magic_name__		,    __magic_name__						)

								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(__magic_name__						)


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


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      ArgumentParser(
				    description=(
				        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
				    )						)

				# Optional arguments for the launch helper
				parser.add_argument("--num_cores"		,    type=__magic_name__		,    default=1		,    help="Number of TPU cores to use (1 or 8)."						)

				# positional
				parser.add_argument(
				    "training_script"		,    type=__magic_name__		,    help=(
				        "The full path to the single TPU training "
				        "program/script to be launched in parallel, "
				        "followed by all the arguments for the "
				        "training script"
				    )		,    )

				# rest from the training program
				parser.add_argument("training_script_args"		,    nargs=__magic_name__						)

				return parser.parse_args()

def  __snake_case							(	):







				'''simple docstring'''


				lowercase         =      parse_args()

				# Import training_script as a module.
				lowercase         =      Path(args.training_script						)
				sys.path.append(str(script_fpath.parent.resolve()						)						)
				lowercase         =      script_fpath.stem
				lowercase         =      importlib.import_module(__magic_name__						)

				# Patch sys.argv
				lowercase         =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores						)]

				xmp.spawn(mod._mp_fn		,    args=()		,    nprocs=args.num_cores						)


if __name__ == "__main__":
						main()
