






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


__magic_name__:					List[str]							=     logging.get_logger(__name__)
logging.set_verbosity_info()

def    UpperCamelCase						(	_A,   _A			):






						"""simple docstring"""







						if "xprophetnet" in prophetnet_checkpoint_path:
												__magic_name__      :       int       						= XLMProphetNetForConditionalGenerationOld.from_pretrained(_A			)
												__magic_name__						,__magic_name__      :       Tuple       						= XLMProphetNetForConditionalGeneration.from_pretrained(
												    _A,   output_loading_info=_A			)
						else:
												__magic_name__      :       int       						= ProphetNetForConditionalGenerationOld.from_pretrained(_A			)
												__magic_name__						,__magic_name__      :       Union[str, Any]       						= ProphetNetForConditionalGeneration.from_pretrained(
												    _A,   output_loading_info=_A			)

						__magic_name__      :       Any       						= ["""key_proj""", """value_proj""", """query_proj"""]

						__magic_name__      :       List[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"]:
												__magic_name__      :       List[Any]       						= key.split("""."""			)

												if attributes[0] == "lm_head":
																		__magic_name__      :       Optional[int]       						= prophet
																		__magic_name__      :       Tuple       						= prophet_old
												else:
																		__magic_name__      :       List[str]       						= prophet.prophetnet
																		__magic_name__      :       Optional[Any]       						= prophet_old.model

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

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

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

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

																								if old_attribute == "":
																														__magic_name__      :       List[Any]       						= old_model
																								else:
																														if not hasattr(_A,   _A			):
																																				raise ValueError(f'{old_model} does not have {old_attribute}'			)
																														__magic_name__      :       Union[str, Any]       						= getattr(_A,   _A			)

												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(_A			)


if __name__ == "__main__":
		__magic_name__:					Optional[int]							=     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."
		)
		__magic_name__:					Any							=     parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
