





"""simple docstring"""






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A 			=  logging.get_logger(__name__)




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  ['''input_ids''', '''attention_mask''']







					def __init__(						self	,			_UpperCAmelCase="</s>"	,			_UpperCAmelCase="<unk>"	,			_UpperCAmelCase="<pad>"	,			_UpperCAmelCase=125	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
													__a :						Union[str, Any]			       =  [f"""<extra_id_{i}>""" for i in range(_UpperCAmelCase     )]
									elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													__a :						Optional[int]			       =  len(set(filter(lambda _UpperCAmelCase     : bool('''extra_id''' in str(_UpperCAmelCase     )     )	,			_UpperCAmelCase     )     )     )
													if extra_tokens != extra_ids:
																	raise ValueError(
																	    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																	    ''' provided to ByT5Tokenizer. In this case the additional_special_tokens must include the'''
																	    ''' extra_ids tokens'''     )

									__a :						Union[str, Any]			       =  AddedToken(_UpperCAmelCase	,			lstrip=_UpperCAmelCase	,			rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) else pad_token
									__a :						List[Any]			       =  AddedToken(_UpperCAmelCase	,			lstrip=_UpperCAmelCase	,			rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) else eos_token
									__a :						Tuple			       =  AddedToken(_UpperCAmelCase	,			lstrip=_UpperCAmelCase	,			rstrip=_UpperCAmelCase     ) if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) else unk_token

									super().__init__(
									    eos_token=_UpperCAmelCase	,			unk_token=_UpperCAmelCase	,			pad_token=_UpperCAmelCase	,			extra_ids=_UpperCAmelCase	,			additional_special_tokens=_UpperCAmelCase	,			**_UpperCAmelCase	,			)

									__a :						List[str]			       =  extra_ids

									__a :						List[str]			       =  2**8  # utf is 8 bits

									# define special tokens dict
									__a :						Dict[int, str]			       =  {
									    self.pad_token: 0,
									    self.eos_token: 1,
									    self.unk_token: 2,
									}
									__a :						Dict			       =  len(self.special_tokens_encoder     )
									__a :						Union[str, Any]			       =  len(_UpperCAmelCase     )
									for i, token in enumerate(_UpperCAmelCase     ):
													__a :						Union[str, Any]			       =  self.vocab_size + i - n
									__a :						Dict[str, int]			       =  {v: k for k, v in self.special_tokens_encoder.items()}







					@property
					def  _lowerCamelCase		(						self     ):
									return self._utf_vocab_size + self._num_special_tokens + self._extra_ids







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False     ):
									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=_UpperCAmelCase	,			token_ids_a=_UpperCAmelCase	,			already_has_special_tokens=_UpperCAmelCase     )

									# normal case: some special tokens
									if token_ids_a is None:
													return ([0] * len(_UpperCAmelCase     )) + [1]
									return ([0] * len(_UpperCAmelCase     )) + [1] + ([0] * len(_UpperCAmelCase     )) + [1]







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if len(_UpperCAmelCase     ) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
													    ''' eos tokens being added.'''     )
													return token_ids
									else:
													return token_ids + [self.eos_token_id]







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									__a :						Any			       =  [self.eos_token_id]

									if token_ids_a is None:
													return len(token_ids_a + eos     ) * [0]
									return len(token_ids_a + eos + token_ids_a + eos     ) * [0]







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									__a :						Union[str, Any]			       =  self._add_eos_if_not_present(_UpperCAmelCase     )
									if token_ids_a is None:
													return token_ids_a
									else:
													__a :						Any			       =  self._add_eos_if_not_present(_UpperCAmelCase     )
													return token_ids_a + token_ids_a







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						List[Any]			       =  [chr(_UpperCAmelCase     ) for i in text.encode('''utf-8'''     )]
									return tokens







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if token in self.special_tokens_encoder:
													__a :						Optional[int]			       =  self.special_tokens_encoder[token]
									elif token in self.added_tokens_encoder:
													__a :						Optional[int]			       =  self.added_tokens_encoder[token]
									elif len(_UpperCAmelCase     ) != 1:
													__a :						Optional[int]			       =  self.unk_token_id
									else:
													__a :						List[Any]			       =  ord(_UpperCAmelCase     ) + self._num_special_tokens
									return token_id







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if index in self.special_tokens_decoder:
													__a :						Optional[Any]			       =  self.special_tokens_decoder[index]
									else:
													__a :						int			       =  chr(index - self._num_special_tokens     )
									return token







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  b''''''
									for token in tokens:
													if token in self.special_tokens_decoder:
																	__a :						str			       =  self.special_tokens_decoder[token].encode('''utf-8'''     )
													elif token in self.added_tokens_decoder:
																	__a :						List[str]			       =  self.special_tokens_decoder[token].encode('''utf-8'''     )
													elif token in self.special_tokens_encoder:
																	__a :						Tuple			       =  token.encode('''utf-8'''     )
													elif token in self.added_tokens_encoder:
																	__a :						Optional[int]			       =  token.encode('''utf-8'''     )
													else:
																	__a :						List[Any]			       =  bytes([ord(_UpperCAmelCase     )]     )
													bstring += tok_string
									__a :						Optional[Any]			       =  bstring.decode('''utf-8'''	,			errors='''ignore'''     )
									return string







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									return ()






"""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      (			a_						:str   ,       a_						:str)		->		List[Any]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								__a :						List[str]			       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						int			       =  XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)
				else:
								__a :						Optional[int]			       =  ProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						Optional[Any]			       =  ProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)

				__a :						Tuple			       =  ['''key_proj''', '''value_proj''', '''query_proj''']

				__a :						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"]:
								__a :						List[str]			       =  key.split('''.''')

								if attributes[0] == "lm_head":
												__a :						Dict			       =  prophet
												__a :						Optional[Any]			       =  prophet_old
								else:
												__a :						Union[str, Any]			       =  prophet.prophetnet
												__a :						int			       =  prophet_old.model

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

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

																elif attribute == "key_proj":
																				__a :						Union[str, Any]			       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
																				__a :						int			       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
																elif attribute == "value_proj":
																				__a :						Dict			       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
																__a :						int			       =  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_12, "We want 512 position_embeddings."
																__a :						Optional[Any]			       =  nn.Parameter(old_model.embed_positions.weight[:5_12, :])
																__a :						List[Any]			       =  True
																break

												if attribute.isdigit():
																__a :						Optional[Any]			       =  model[int(a_)]
																__a :						str			       =  old_model[int(a_)]
												else:
																__a :						Optional[int]			       =  getattr(a_   ,       a_)

																if old_attribute == "":
																				__a :						str			       =  old_model
																else:
																				if not hasattr(a_   ,       a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a :						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__":
		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)