"""simple docstring"""






import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_A   					=    object()

# For specifying empty leaf dict `{}`
_A   					=    object()




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       ):
				SCREAMING_SNAKE_CASE__		  =				tuple((re.compile(x + "$"       ) for x in qs)       )
				for i in range(len(__UpperCAmelCase       ) - len(__UpperCAmelCase       ) + 1       ):
								SCREAMING_SNAKE_CASE__		  =				[x.match(__UpperCAmelCase       ) for x, y in zip(__UpperCAmelCase  ,					ks[i:]       )]
								if matches and all(__UpperCAmelCase       ):
												return True
				return False




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       ):

				def replace(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								for rule, replacement in rules:
												if _match(__UpperCAmelCase  ,					__UpperCAmelCase       ):
																return replacement
								return val

				return replace




def 						SCREAMING_SNAKE_CASE					(  ):
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P("mp"  ,					__UpperCAmelCase       )),
				    (("transformer", "wte", "embedding"), P("mp"  ,					__UpperCAmelCase       )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(__UpperCAmelCase  ,					"mp"       )),
				    (("attention", "out_proj", "kernel"), P("mp"  ,					__UpperCAmelCase       )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(__UpperCAmelCase  ,					"mp"       )),
				    (("mlp", "c_fc", "bias"), P("mp"       )),
				    (("mlp", "c_proj", "kernel"), P("mp"  ,					__UpperCAmelCase       )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       ):
				SCREAMING_SNAKE_CASE__		  =				_get_partition_rules()
				SCREAMING_SNAKE_CASE__		  =				_replacement_rules(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				{k: _unmatched for k in flatten_dict(__UpperCAmelCase       )}
				SCREAMING_SNAKE_CASE__		  =				{k: replace(__UpperCAmelCase  ,					__UpperCAmelCase       ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(__UpperCAmelCase       )       )





"""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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   List[str]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				XLMProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )
				else:
								SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				ProphetNetForConditionalGeneration.from_pretrained(
								    __UpperCAmelCase  ,					output_loading_info=__UpperCAmelCase       )

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

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

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

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

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

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

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

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

								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(__UpperCAmelCase       )


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)




