





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


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






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				str       , UpperCamelCase__:				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__":
	_lowerCamelCase 					=      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.'
	)
	_lowerCamelCase 					=      parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






from typing import Dict, List, Optional

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


_lowerCamelCase 					=      logging.get_logger(__name__)


_lowerCamelCase 					=      {
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase 					=      1114112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCamelCase 					=      0

_lowerCamelCase 					=      0XE0_00
_lowerCamelCase 					=      0XE0_01
_lowerCamelCase 					=      0XE0_02
_lowerCamelCase 					=      0XE0_03
_lowerCamelCase 					=      0XE0_04

# Maps special codepoints to human-readable names.
_lowerCamelCase 					=      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCamelCase 					=      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


							def __init__(				self     :str				,					__A     :str=chr(__A		)				,					__A     :str=chr(__A		)				,					__A     :Dict=chr(__A		)				,					__A     :str=chr(__A		)				,					__A     :Union[str, Any]=chr(__A		)				,					__A     :str=chr(__A		)				,					__A     :int=False				,					__A     :int=2048				,					**__A     :Dict				,					) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else bos_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else eos_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else sep_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else cls_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else mask_token

									super().__init__(
									    bos_token=__A				,					eos_token=__A				,					sep_token=__A				,					cls_token=__A				,					pad_token=__A				,					mask_token=__A				,					add_prefix_space=__A				,					model_max_length=__A				,					**__A				,					)

									# Creates a mapping for looking up the IDs of special symbols.
									SCREAMING_SNAKE_CASE__								=   {}
									for codepoint, name in SPECIAL_CODEPOINTS.items():
											SCREAMING_SNAKE_CASE__								=   codepoint

									# Creates a mapping for looking up the string forms of special symbol IDs.
									SCREAMING_SNAKE_CASE__								=   {
									    codepoint: name for name, codepoint in self._special_codepoints.items()
									}

									SCREAMING_SNAKE_CASE__								=   UNICODE_VOCAB_SIZE
									SCREAMING_SNAKE_CASE__								=   len(self._special_codepoints		)


							@property
							def   _snake_case	(				self     :Optional[Any]		) -> int:



									"""simple docstring"""



									return self._unicode_vocab_size


							def   _snake_case	(				self     :Tuple				,					__A     :str		) -> List[str]:



									"""simple docstring"""



									return list(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :str		) -> int:



									"""simple docstring"""



									try:
											return ord(__A		)
									except TypeError:
											raise ValueError(f'''invalid token: \'{token}\''''		)


							def   _snake_case	(				self     :str				,					__A     :int		) -> str:



									"""simple docstring"""



									try:
											if index in SPECIAL_CODEPOINTS:
													return SPECIAL_CODEPOINTS[index]
											return chr(__A		)
									except TypeError:
											raise ValueError(f'''invalid id: {index}'''		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Optional[int]		) -> Any:



									"""simple docstring"""



									return "".join(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [self.cls_token_id]

									SCREAMING_SNAKE_CASE__								=   cls + token_ids_a + sep
									if token_ids_a is not None:
											result += token_ids_a + sep
									return result


							def   _snake_case	(				self     :List[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None				,					__A     :bool = False		) -> List[int]:



									"""simple docstring"""



									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__A				,					token_ids_a=__A				,					already_has_special_tokens=__A		)

									SCREAMING_SNAKE_CASE__								=   [1] + ([0] * len(__A		)) + [1]
									if token_ids_a is not None:
											result += ([0] * len(__A		)) + [1]
									return result


							def   _snake_case	(				self     :List[str]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [self.cls_token_id]

									SCREAMING_SNAKE_CASE__								=   len(cls + token_ids_a + sep		) * [0]
									if token_ids_a is not None:
											result += len(token_ids_a + sep		) * [1]
									return result


							def   _snake_case	(				self     :int				,					__A     :str				,					__A     :Optional[str] = None		) -> Any:



									"""simple docstring"""



									return ()