



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


_lowercase		:				str      		=logging.get_logger(__name__)
logging.set_verbosity_info()

def      lowerCAmelCase_					(  _lowercase    :       str						,       _lowercase    :       str)							->	Tuple:







			"""simple docstring"""
			if "xprophetnet" in prophetnet_checkpoint_path:
						a__       :						str							=  XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowercase)
						a__	,						a__       :						List[str]							=  XLMProphetNetForConditionalGeneration.from_pretrained(
						    _lowercase						,       output_loading_info=_lowercase)
			else:
						a__       :						Optional[Any]							=  ProphetNetForConditionalGenerationOld.from_pretrained(_lowercase)
						a__	,						a__       :						Optional[Any]							=  ProphetNetForConditionalGeneration.from_pretrained(
						    _lowercase						,       output_loading_info=_lowercase)

			a__       :						Dict							=  ["""key_proj""", """value_proj""", """query_proj"""]

			a__       :						Dict							=  {
			    """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__       :						Any							=  key.split(""".""")

						if attributes[0] == "lm_head":
									a__       :						List[str]							=  prophet
									a__       :						Union[str, Any]							=  prophet_old
						else:
									a__       :						Any							=  prophet.prophetnet
									a__       :						List[str]							=  prophet_old.model

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

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

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

									if attribute.isdigit():
												a__       :						List[str]							=  model[int(_lowercase)]
												a__       :						Optional[int]							=  old_model[int(_lowercase)]
									else:
												a__       :						int							=  getattr(_lowercase						,       _lowercase)

												if old_attribute == "":
															a__       :						List[Any]							=  old_model
												else:
															if not hasattr(_lowercase						,       _lowercase):
																		raise ValueError(F'''{old_model} does not have {old_attribute}''')
															a__       :						Any							=  getattr(_lowercase						,       _lowercase)

						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(_lowercase)


if __name__ == "__main__":
		_lowercase		:				List[Any]      		=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."
		)
		_lowercase		:				str      		=parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
		import ctypes
		import msvcrt  # noqa

		class        snake_case__    (ctypes.Structure		):







								"""simple docstring"""




								__lowerCAmelCase					:Dict      =					[("size", ctypes.c_int), ("visible", ctypes.c_byte)]

def      lowerCAmelCase_					(  )							->	List[Any]:







			"""simple docstring"""
			if os.name == "nt":
						a__       :						int							=  CursorInfo()
						a__       :						Union[str, Any]							=  ctypes.windll.kernelaa.GetStdHandle(-11)
						ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
						a__       :						List[str]							=  False
						ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
			elif os.name == "posix":
						sys.stdout.write("""\033[?25l""")
						sys.stdout.flush()

def      lowerCAmelCase_					(  )							->	Optional[Any]:







			"""simple docstring"""
			if os.name == "nt":
						a__       :						List[Any]							=  CursorInfo()
						a__       :						Optional[int]							=  ctypes.windll.kernelaa.GetStdHandle(-11)
						ctypes.windll.kernelaa.GetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
						a__       :						Dict							=  True
						ctypes.windll.kernelaa.SetConsoleCursorInfo(_lowercase						,       ctypes.byref(_lowercase))
			elif os.name == "posix":
						sys.stdout.write("""\033[?25h""")
						sys.stdout.flush()





@contextmanager
def      lowerCAmelCase_					(  )							->	Any:







			"""simple docstring"""
			try:
						hide_cursor()
						yield
			finally:
						show_cursor()




