



"""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:   List[Any]								=		logging.get_logger(__name__)
logging.set_verbosity_info()

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		str						):
 if "xprophetnet" in prophetnet_checkpoint_path:
  UpperCAmelCase	:					Optional[int]	=	XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase						)
  UpperCAmelCase	,		UpperCAmelCase	:					int	=	XLMProphetNetForConditionalGeneration.from_pretrained(
      UpperCamelCase			,		output_loading_info=UpperCamelCase						)
 else:
  UpperCAmelCase	:					Tuple	=	ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase						)
  UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	ProphetNetForConditionalGeneration.from_pretrained(
      UpperCamelCase			,		output_loading_info=UpperCamelCase						)

 UpperCAmelCase	:					Tuple	=	["""key_proj""", """value_proj""", """query_proj"""]

 UpperCAmelCase	:					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"]:
  UpperCAmelCase	:					Union[str, Any]	=	key.split("""."""						)

  if attributes[0] == "lm_head":
   UpperCAmelCase	:					int	=	prophet
   UpperCAmelCase	:					Dict	=	prophet_old
  else:
   UpperCAmelCase	:					int	=	prophet.prophetnet
   UpperCAmelCase	:					Dict	=	prophet_old.model

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

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

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

   if attribute.isdigit():
    UpperCAmelCase	:					str	=	model[int(UpperCamelCase						)]
    UpperCAmelCase	:					List[Any]	=	old_model[int(UpperCamelCase						)]
   else:
    UpperCAmelCase	:					Any	=	getattr(UpperCamelCase			,		UpperCamelCase						)

    if old_attribute == "":
     UpperCAmelCase	:					Dict	=	old_model
    else:
     if not hasattr(UpperCamelCase			,		UpperCamelCase						):
      raise ValueError(F"{old_model} does not have {old_attribute}"						)
     UpperCAmelCase	:					List[Any]	=	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__":
      A:   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."
      )
      A:   Optional[Any]								=		parser.parse_args()
      convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""
from collections.abc import Callable

import numpy as np

def       _snake_case						(						UpperCamelCase						:		Callable			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 UpperCAmelCase	:					Any	=	int(np.ceil((x_end - xa) / step_size						)						)
 UpperCAmelCase	:					Optional[Any]	=	np.zeros((n + 1,)						)
 UpperCAmelCase	:					Optional[int]	=	ya
 UpperCAmelCase	:					int	=	xa

 for k in range(UpperCamelCase						):
  UpperCAmelCase	:					Optional[int]	=	y[k] + step_size * ode_func(UpperCamelCase			,		y[k]						)
  UpperCAmelCase	:					Optional[int]	=	y[k] + (
      (step_size / 2) * (ode_func(UpperCamelCase			,		y[k]						) + ode_func(x + step_size			,		UpperCamelCase						))
  )
  x += step_size

 return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()
