



"""simple docstring"""






from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)






class 	__snake_case	(     _lowercase):




  def __init__(			self					:						Any						, __lowerCAmelCase					:						Distribution						, __lowerCAmelCase					:						List[Any]=None						, __lowerCAmelCase					:						Union[str, Any]=None						, __lowerCAmelCase					:						Dict=0		):






         """simple docstring"""






         _lowerCamelCase						:    List[Any]         =			1.0 if scale is None else scale
         _lowerCamelCase						:    Union[str, Any]         =			0.0 if loc is None else loc

         super().__init__(__lowerCAmelCase						, [AffineTransform(loc=self.loc						, scale=self.scale						, event_dim=__lowerCAmelCase		)]		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         return self.base_dist.mean * self.scale + self.loc




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         return self.base_dist.variance * self.scale**2




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         return self.variance.sqrt()






class 	__snake_case	(     nn.Module):




  def __init__(			self					:						Any						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Dict[str, int]						, __lowerCAmelCase					:						Callable[..., Tuple[torch.Tensor]]						, **__lowerCAmelCase					:						Any		):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase		)
         _lowerCamelCase						:    str         =			args_dim
         _lowerCamelCase						:    Dict         =			nn.ModuleList([nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		) for dim in args_dim.values()]		)
         _lowerCamelCase						:    List[Any]         =			domain_map




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						torch.Tensor		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			[proj(__lowerCAmelCase		) for proj in self.proj]

         return self.domain_map(*__lowerCAmelCase		)






class 	__snake_case	(     nn.Module):




  def __init__(			self					:						List[Any]						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase						:    str         =			function




  def 						SCREAMING_SNAKE_CASE    (			self					:						Union[str, Any]						, __lowerCAmelCase					:						Optional[int]						, *__lowerCAmelCase					:						int		):






         """simple docstring"""






         return self.function(__lowerCAmelCase						, *__lowerCAmelCase		)






class 	__snake_case	:
  snake_case__    :       type
  snake_case__    :       int
  snake_case__    :       Dict[str, int]




  def __init__(			self					:						int						, __lowerCAmelCase					:						int = 1		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			dim
         _lowerCamelCase						:    Optional[int]         =			{k: dim * self.args_dim[k] for k in self.args_dim}




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						Any		):






         """simple docstring"""






         if self.dim == 1:
                return self.distribution_class(*__lowerCAmelCase		)
         else:
                return Independent(self.distribution_class(*__lowerCAmelCase		)						, 1		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Tuple						, __lowerCAmelCase					:						Optional[torch.Tensor] = None						, __lowerCAmelCase					:						Optional[torch.Tensor] = None						, ):






         """simple docstring"""






         _lowerCamelCase						:    int         =			self._base_distribution(__lowerCAmelCase		)
         if loc is None and scale is None:
                return distr
         else:
                return AffineTransformed(__lowerCAmelCase						, loc=__lowerCAmelCase						, scale=__lowerCAmelCase						, event_dim=self.event_dim		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						int		):






         """simple docstring"""






         return () if self.dim == 1 else (self.dim,)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[int]		):






         """simple docstring"""






         return len(self.event_shape		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         return 0.0




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						int		):






         """simple docstring"""






         return ParameterProjection(
             in_features=__lowerCAmelCase						, args_dim=self.args_dim						, domain_map=LambdaLayer(self.domain_map		)						, )




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, *__lowerCAmelCase					:						torch.Tensor		):






         """simple docstring"""






         raise NotImplementedError()




  @staticmethod
  def 						SCREAMING_SNAKE_CASE    (			__lowerCAmelCase					:						torch.Tensor		):






         """simple docstring"""






         return (x + torch.sqrt(torch.square(__lowerCAmelCase		) + 4.0		)) / 2.0






class 	__snake_case	(     _lowercase):
  snake_case__    :       Dict[str, int]        =						{"df": 1, "loc": 1, "scale": 1}
  snake_case__    :       type        =						StudentT




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Optional[int]						, __lowerCAmelCase					:						torch.Tensor						, __lowerCAmelCase					:						torch.Tensor						, __lowerCAmelCase					:						torch.Tensor		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			cls.squareplus(__lowerCAmelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
         _lowerCamelCase						:    Dict         =			2.0 + cls.squareplus(__lowerCAmelCase		)
         return df.squeeze(-1		), loc.squeeze(-1		), scale.squeeze(-1		)






class 	__snake_case	(     _lowercase):
  snake_case__    :       Dict[str, int]        =						{"loc": 1, "scale": 1}
  snake_case__    :       type        =						Normal




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Optional[int]						, __lowerCAmelCase					:						torch.Tensor						, __lowerCAmelCase					:						torch.Tensor		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			cls.squareplus(__lowerCAmelCase		).clamp_min(torch.finfo(scale.dtype		).eps		)
         return loc.squeeze(-1		), scale.squeeze(-1		)






class 	__snake_case	(     _lowercase):
  snake_case__    :       Dict[str, int]        =						{"total_count": 1, "logits": 1}
  snake_case__    :       type        =						NegativeBinomial




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Optional[int]						, __lowerCAmelCase					:						torch.Tensor						, __lowerCAmelCase					:						torch.Tensor		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			cls.squareplus(__lowerCAmelCase		)
         return total_count.squeeze(-1		), logits.squeeze(-1		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Optional[Any]						, __lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Union[str, Any]         =			distr_args
         if self.dim == 1:
                return self.distribution_class(total_count=__lowerCAmelCase						, logits=__lowerCAmelCase		)
         else:
                return Independent(self.distribution_class(total_count=__lowerCAmelCase						, logits=__lowerCAmelCase		)						, 1		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, __lowerCAmelCase					:						List[Any]						, __lowerCAmelCase					:						Optional[torch.Tensor] = None						, __lowerCAmelCase					:						Optional[torch.Tensor] = None		):






         """simple docstring"""






         _lowerCamelCase						:    Any         =			distr_args

         if scale is not None:
                # See scaling property of Gamma.
                logits += scale.log()

         return self._base_distribution((total_count, logits)		)






"""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


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






def 	snake_case_  (				A_ :		str,    A_ :		str						):


       '''simple docstring'''





       if "xprophetnet" in prophetnet_checkpoint_path:
              _lowerCamelCase						:    Any         =			XLMProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    List[str]         =			XLMProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)
       else:
              _lowerCamelCase						:    str         =			ProphetNetForConditionalGenerationOld.from_pretrained(A_						)
              _lowerCamelCase      ,   _lowerCamelCase						:    Any         =			ProphetNetForConditionalGeneration.from_pretrained(
                  A_,    output_loading_info=A_						)

       _lowerCamelCase						:    Optional[Any]         =			['''key_proj''', '''value_proj''', '''query_proj''']

       _lowerCamelCase						:    List[Any]         =			{
           '''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"]:
              _lowerCamelCase						:    Union[str, Any]         =			key.split('''.'''						)

              if attributes[0] == "lm_head":
                     _lowerCamelCase						:    str         =			prophet
                     _lowerCamelCase						:    List[Any]         =			prophet_old
              else:
                     _lowerCamelCase						:    Optional[int]         =			prophet.prophetnet
                     _lowerCamelCase						:    Optional[Any]         =			prophet_old.model

              _lowerCamelCase						:    Any         =			False
              for attribute in attributes:
                     if attribute in mapping:
                            _lowerCamelCase						:    Optional[int]         =			mapping[attribute]
                            if not hasattr(A_,    A_						) and len(A_						) > 0:
                                   _lowerCamelCase						:    int         =			attribute
                     elif hasattr(A_,    A_						):
                            _lowerCamelCase						:    int         =			attribute

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

                            elif attribute == "key_proj":
                                   _lowerCamelCase						:    int         =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]						)
                            elif attribute == "value_proj":
                                   _lowerCamelCase						:    Tuple         =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]						)
                                   _lowerCamelCase						:    str         =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]						)
                            _lowerCamelCase						:    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] == 5_12, "We want 512 position_embeddings."
                            _lowerCamelCase						:    List[str]         =			nn.Parameter(old_model.embed_positions.weight[:5_12, :]						)
                            _lowerCamelCase						:    Optional[Any]         =			True
                            break

                     if attribute.isdigit():
                            _lowerCamelCase						:    Optional[int]         =			model[int(A_						)]
                            _lowerCamelCase						:    List[Any]         =			old_model[int(A_						)]
                     else:
                            _lowerCamelCase						:    List[str]         =			getattr(A_,    A_						)

                            if old_attribute == "":
                                   _lowerCamelCase						:    str         =			old_model
                            else:
                                   if not hasattr(A_,    A_						):
                                          raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
                                   _lowerCamelCase						:    Optional[int]         =			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__":
   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)


