


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					str				):
     if "xprophetnet" in prophetnet_checkpoint_path:
          __UpperCamelCase    							=XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase							,      __UpperCamelCase    							=XLMProphetNetForConditionalGeneration.from_pretrained(
              SCREAMING_SNAKE_CASE__			,     output_loading_info=SCREAMING_SNAKE_CASE__				)
     else:
          __UpperCamelCase    							=ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase							,      __UpperCamelCase    							=ProphetNetForConditionalGeneration.from_pretrained(
              SCREAMING_SNAKE_CASE__			,     output_loading_info=SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=['key_proj', 'value_proj', 'query_proj']

     __UpperCamelCase    							={
         '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    							=key.split('.'				)

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

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

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

                    elif attribute == "key_proj":
                         __UpperCamelCase    							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]				)
                         __UpperCamelCase    							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]				)
                    elif attribute == "value_proj":
                         __UpperCamelCase    							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]				)
                         __UpperCamelCase    							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]				)
                    __UpperCamelCase    							=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."
                    __UpperCamelCase    							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]				)
                    __UpperCamelCase    							=True
                    break

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

                    if old_attribute == "":
                         __UpperCamelCase    							=old_model
                    else:
                         if not hasattr(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
                              raise ValueError(F'{old_model} does not have {old_attribute}'				)
                         __UpperCamelCase    							=getattr(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)

          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(SCREAMING_SNAKE_CASE__				)


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)









"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__(					self				:    List[str]				, __UpperCAmelCase				:    int = 0):
      a						:		Tuple      							=			key






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		Dict      							=			key or self.__key or 1

      # make sure key is an appropriate size
      key %= 255

      return [chr(ord(__UpperCAmelCase) ^ key) for ch in content]






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		Optional[Any]      							=			key or self.__key or 1

      # make sure key is an appropriate size
      key %= 255

      return [chr(ord(__UpperCAmelCase) ^ key) for ch in content]






    def 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int = 0):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		List[Any]      							=			key or self.__key or 1

      # make sure key can be any size
      while key > 255:
        key -= 255

      # This will be returned
      a						:		Any      							=			""

      for ch in content:
        ans += chr(ord(__UpperCAmelCase) ^ key)

      return ans






    def 	__snake_case							(					self				:    List[str]				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int = 0):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      a						:		Dict      							=			key or self.__key or 1

      # make sure key can be any size
      while key > 255:
        key -= 255

      # This will be returned
      a						:		str      							=			""

      for ch in content:
        ans += chr(ord(__UpperCAmelCase) ^ key)

      return ans






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int = 0):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      try:
        with open(__UpperCAmelCase) as fin, open("encrypt.out"				, "w+") as fout:
          # actual encrypt-process
          for line in fin:
            fout.write(self.encrypt_string(__UpperCAmelCase				, __UpperCAmelCase))

      except OSError:
        return False

      return True






    def 	__snake_case							(					self				:    Any				, __UpperCAmelCase				:    str				, __UpperCAmelCase				:    int):
      assert isinstance(__UpperCAmelCase				, __UpperCAmelCase) and isinstance(__UpperCAmelCase				, __UpperCAmelCase)

      try:
        with open(__UpperCAmelCase) as fin, open("decrypt.out"				, "w+") as fout:
          # actual encrypt-process
          for line in fin:
            fout.write(self.decrypt_string(__UpperCAmelCase				, __UpperCAmelCase))

      except OSError:
        return False

      return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")






