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


SCREAMING_SNAKE_CASE :	Optional[int]							     =		logging.get_logger(__name__)
logging.set_verbosity_info()



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if "xprophetnet" in prophetnet_checkpoint_path:
  __a       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(_A					)
  __a       ,     __a       =  XLMProphetNetForConditionalGeneration.from_pretrained(
      _A							,				output_loading_info=_A					)
 else:
  __a       =  ProphetNetForConditionalGenerationOld.from_pretrained(_A					)
  __a       ,     __a       =  ProphetNetForConditionalGeneration.from_pretrained(
      _A							,				output_loading_info=_A					)

 __a       =  ["key_proj", "value_proj", "query_proj"]

 __a       =  {
     "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       =  key.split("."					)

  if attributes[0] == "lm_head":
   __a       =  prophet
   __a       =  prophet_old
  else:
   __a       =  prophet.prophetnet
   __a       =  prophet_old.model

  __a       =  False
  for attribute in attributes:
   if attribute in mapping:
    __a       =  mapping[attribute]
    if not hasattr(_A							,				_A					) and len(_A					) > 0:
     __a       =  attribute
   elif hasattr(_A							,				_A					):
    __a       =  attribute

   if attribute == "weight":
    assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
    __a       =  old_model.weight
    logger.info(f"""{attribute} is initialized."""					)
    __a       =  True
    break
   elif attribute == "bias":
    assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
    __a       =  old_model.bias
    logger.info(f"""{attribute} is initialized"""					)
    __a       =  True
    break
   elif attribute in special_keys and hasattr(_A							,				"in_proj_weight"					):
    __a       =  old_model.in_proj_weight.shape[0] // 3
    __a       =  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       =  nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
     __a       =  nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

    elif attribute == "key_proj":
     __a       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
     __a       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
    elif attribute == "value_proj":
     __a       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
     __a       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
    __a       =  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       =  nn.Parameter(old_model.embed_positions.weight[:512, :]					)
    __a       =  True
    break

   if attribute.isdigit():
    __a       =  model[int(_A					)]
    __a       =  old_model[int(_A					)]
   else:
    __a       =  getattr(_A							,				_A					)

    if old_attribute == "":
     __a       =  old_model
    else:
     if not hasattr(_A							,				_A					):
      raise ValueError(f"""{old_model} does not have {old_attribute}"""					)
     __a       =  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__":
  SCREAMING_SNAKE_CASE :	int							     =		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."""
  )
  SCREAMING_SNAKE_CASE :	Dict							     =		parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







from __future__ import annotations
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  order

   # a_{0} ... a_{k}
   __a       =  [1.0] + [0.0] * order
   # b_{0} ... b_{k}
   __a       =  [1.0] + [0.0] * order

   # x[n-1] ... x[n-k]
   __a       =  [0.0] * self.order
   # y[n-1] ... y[n-k]
   __a       =  [0.0] * self.order

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      list[float]     ,							__SCREAMING_SNAKE_CASE :      list[float]       ):
   if len(__SCREAMING_SNAKE_CASE       ) < self.order:
    __a       =  [1.0, *a_coeffs]

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected a_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected b_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   __a       =  a_coeffs
   __a       =  b_coeffs

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      float       ):
   __a       =  0.0

   # Start at index 1 and do index 0 at the end.
   for i in range(1     ,							self.order + 1       ):
    result += (
        self.b_coeffs[i] * self.input_history[i - 1]
        - self.a_coeffs[i] * self.output_history[i - 1]
    )

   __a       =  (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

   __a       =  self.input_history[:-1]
   __a       =  self.output_history[:-1]

   __a       =  sample
   __a       =  result

   return result


