
'''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


snake_case_ :  str     =					logging.get_logger(__name__)
logging.set_verbosity_info()



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

   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(_UpperCAmelCase,      _UpperCAmelCase) and len(_UpperCAmelCase) > 0:
               UpperCamelCase       =  attribute
         elif hasattr(_UpperCAmelCase,      _UpperCAmelCase):
            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(_UpperCAmelCase,      '''in_proj_weight'''):
            UpperCamelCase       =  old_model.in_proj_weight.shape[0] // 3
            UpperCamelCase       =  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       =  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] == 512, "We want 512 position_embeddings."
            UpperCamelCase       =  nn.Parameter(old_model.embed_positions.weight[:512, :])
            UpperCamelCase       =  True
            break

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

            if old_attribute == "":
               UpperCamelCase       =  old_model
            else:
               if not hasattr(_UpperCAmelCase,      _UpperCAmelCase):
                  raise ValueError(f'{old_model} does not have {old_attribute}')
               UpperCamelCase       =  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__":
   snake_case_ :  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.'
   )
   snake_case_ :  Dict     =					parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







'''simple docstring'''


def  __snake_case     (    _UpperCAmelCase					:       Optional[int]):
   UpperCamelCase       =  []
   UpperCamelCase       =  []
   UpperCamelCase       =  {
       '''^''': 3,
       '''*''': 2,
       '''/''': 2,
       '''%''': 2,
       '''+''': 1,
       '''-''': 1,
   }  # Priority of each operator
   UpperCamelCase       =  len(_UpperCAmelCase) if (len(_UpperCAmelCase) > 7) else 7

   # Print table header for output
   print(
       '''Symbol'''.center(8),      '''Stack'''.center(_UpperCAmelCase),      '''Postfix'''.center(_UpperCAmelCase),      sep=''' | ''',      )
   print('''-''' * (print_width * 3 + 7))

   for x in infix:
      if x.isalpha() or x.isdigit():
         post_fix.append(_UpperCAmelCase)  # if x is Alphabet / Digit, add it to Postfix
      elif x == "(":
         stack.append(_UpperCAmelCase)  # if x is "(" push to Stack
      elif x == ")":  # if x is ")" pop stack until "(" is encountered
         while stack[-1] != "(":
            post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
         stack.pop()
      else:
         if len(_UpperCAmelCase) == 0:
            stack.append(_UpperCAmelCase)  # If stack is empty, push x to stack
         else:  # while priority of x is not > priority of element in the stack
            while len(_UpperCAmelCase) > 0 and priority[x] <= priority[stack[-1]]:
               post_fix.append(stack.pop())  # pop stack & add to Postfix
            stack.append(_UpperCAmelCase)  # push x to stack

      print(
          x.center(8),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      sep=''' | ''',      )  # Output in tabular format

   while len(_UpperCAmelCase) > 0:  # while stack is not empty
      post_fix.append(stack.pop())  # pop stack & add to Postfix
      print(
          ''' '''.center(8),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      (''''''.join(_UpperCAmelCase)).ljust(_UpperCAmelCase),      sep=''' | ''',      )  # Output in tabular format

   return "".join(_UpperCAmelCase)  # return Postfix as str



def  __snake_case     (    _UpperCAmelCase					:       str):
   UpperCamelCase       =  list(infix[::-1])  # reverse the infix equation

   for i in range(len(_UpperCAmelCase)):
      if infix[i] == "(":
         UpperCamelCase       =  ''')'''  # change "(" to ")"
      elif infix[i] == ")":
         UpperCamelCase       =  '''('''  # change ")" to "("

   return (infix_2_postfix(''''''.join(_UpperCAmelCase)))[
       ::-1
   ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
   snake_case_ :  str     =					input('\nEnter an Infix Equation = ')  # Input an Infix equation
   snake_case_ :  Union[str, Any]     =					''.join(Infix.split())  # Remove spaces from the input
   print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')





