





"""simple docstring"""






import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
		import onnxruntime as ort


A 			=  logging.get_logger(__name__)

A 			=  {
    '''tensor(bool)''': np.bool_,
    '''tensor(int8)''': np.inta,
    '''tensor(uint8)''': np.uinta,
    '''tensor(int16)''': np.intaa,
    '''tensor(uint16)''': np.uintaa,
    '''tensor(int32)''': np.intaa,
    '''tensor(uint32)''': np.uintaa,
    '''tensor(int64)''': np.intaa,
    '''tensor(uint64)''': np.uintaa,
    '''tensor(float16)''': np.floataa,
    '''tensor(float)''': np.floataa,
    '''tensor(double)''': np.floataa,
}




class 				__lowercase			:







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									logger.info('''`diffusers.OnnxRuntimeModel` is experimental and might change in the future.'''     )
									__a :						Dict			       =  model
									__a :						int			       =  kwargs.get('''model_save_dir'''	,			_UpperCAmelCase     )
									__a :						Optional[Any]			       =  kwargs.get('''latest_model_name'''	,			_UpperCAmelCase     )







					def __call__(						self	,			**_UpperCAmelCase     ):
									__a :						Dict			       =  {k: np.array(_UpperCAmelCase     ) for k, v in kwargs.items()}
									return self.model.run(_UpperCAmelCase	,			_UpperCAmelCase     )







					@staticmethod
					def  _lowerCamelCase		(						_UpperCAmelCase	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None     ):
									if provider is None:
													logger.info('''No onnxruntime provider specified, using CPUExecutionProvider'''     )
													__a :						List[str]			       =  '''CPUExecutionProvider'''

									return ort.InferenceSession(_UpperCAmelCase	,			providers=[provider]	,			sess_options=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			**_UpperCAmelCase     ):
									__a :						int			       =  file_name if file_name is not None else ONNX_WEIGHTS_NAME

									__a :						List[Any]			       =  self.model_save_dir.joinpath(self.latest_model_name     )
									__a :						int			       =  Path(_UpperCAmelCase     ).joinpath(_UpperCAmelCase     )
									try:
													shutil.copyfile(_UpperCAmelCase	,			_UpperCAmelCase     )
									except shutil.SameFileError:
													pass

									# copy external weights (for models >2GB)
									__a :						Optional[Any]			       =  self.model_save_dir.joinpath(_UpperCAmelCase     )
									if src_path.exists():
													__a :						Any			       =  Path(_UpperCAmelCase     ).joinpath(_UpperCAmelCase     )
													try:
																	shutil.copyfile(_UpperCAmelCase	,			_UpperCAmelCase     )
													except shutil.SameFileError:
																	pass







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			**_UpperCAmelCase	,			):
									if os.path.isfile(_UpperCAmelCase     ):
													logger.error(f"""Provided path ({save_directory}) should be a directory, not a file"""     )
													return

									os.makedirs(_UpperCAmelCase	,			exist_ok=_UpperCAmelCase     )

									# saving model weights/files
									self._save_pretrained(_UpperCAmelCase	,			**_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = False	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						Optional[Any]			       =  file_name if file_name is not None else ONNX_WEIGHTS_NAME
									# load model from local directory
									if os.path.isdir(_UpperCAmelCase     ):
													__a :						str			       =  OnnxRuntimeModel.load_model(
													    os.path.join(_UpperCAmelCase	,			_UpperCAmelCase     )	,			provider=_UpperCAmelCase	,			sess_options=_UpperCAmelCase     )
													__a :						List[Any]			       =  Path(_UpperCAmelCase     )
									# load model from hub
									else:
													# download model
													__a :						List[str]			       =  hf_hub_download(
													    repo_id=_UpperCAmelCase	,			filename=_UpperCAmelCase	,			use_auth_token=_UpperCAmelCase	,			revision=_UpperCAmelCase	,			cache_dir=_UpperCAmelCase	,			force_download=_UpperCAmelCase	,			)
													__a :						Optional[Any]			       =  Path(_UpperCAmelCase     ).parent
													__a :						int			       =  Path(_UpperCAmelCase     ).name
													__a :						str			       =  OnnxRuntimeModel.load_model(_UpperCAmelCase	,			provider=_UpperCAmelCase	,			sess_options=_UpperCAmelCase     )
									return cls(model=_UpperCAmelCase	,			**_UpperCAmelCase     )







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase = True	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			**_UpperCAmelCase	,			):
									__a :						Dict			       =  None
									if len(str(_UpperCAmelCase     ).split('''@'''     )     ) == 2:
													__a       ,						__a :						Optional[Any]			       =  model_id.split('''@'''     )

									return cls._from_pretrained(
									    model_id=_UpperCAmelCase	,			revision=_UpperCAmelCase	,			cache_dir=_UpperCAmelCase	,			force_download=_UpperCAmelCase	,			use_auth_token=_UpperCAmelCase	,			**_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     __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)