






'''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


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if "xprophetnet" in prophetnet_checkpoint_path:
      lowercase_						:     int							     =  XLMProphetNetForConditionalGenerationOld.from_pretrained(_UpperCamelCase				)
      lowercase_     ,		lowercase_						:     List[str]							     =  XLMProphetNetForConditionalGeneration.from_pretrained(
          _UpperCamelCase    ,		output_loading_info=_UpperCamelCase				)
   else:
      lowercase_						:     str							     =  ProphetNetForConditionalGenerationOld.from_pretrained(_UpperCamelCase				)
      lowercase_     ,		lowercase_						:     int							     =  ProphetNetForConditionalGeneration.from_pretrained(
          _UpperCamelCase    ,		output_loading_info=_UpperCamelCase				)

   lowercase_						:     Optional[int]							     =  ["key_proj", "value_proj", "query_proj"]

   lowercase_						:     Tuple							     =  {
       "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"]:
      lowercase_						:     Dict							     =  key.split("."				)

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

      lowercase_						:     Any							     =  False
      for attribute in attributes:
         if attribute in mapping:
            lowercase_						:     Union[str, Any]							     =  mapping[attribute]
            if not hasattr(_UpperCamelCase    ,		_UpperCamelCase				) and len(_UpperCamelCase				) > 0:
               lowercase_						:     str							     =  attribute
         elif hasattr(_UpperCamelCase    ,		_UpperCamelCase				):
            lowercase_						:     List[Any]							     =  attribute

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

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

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

            if old_attribute == "":
               lowercase_						:     Dict							     =  old_model
            else:
               if not hasattr(_UpperCamelCase    ,		_UpperCamelCase				):
                  raise ValueError(F"""{old_model} does not have {old_attribute}"""				)
               lowercase_						:     List[str]							     =  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__":
 UpperCamelCase__			=      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.'
 )
 UpperCamelCase__			=      parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)












'''simple docstring'''
import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


UpperCamelCase__			=      False
try:
 UpperCamelCase__			=      _is_package_available('google.colab')
except ModuleNotFoundError:
 pass


@input.register
class 							_UpperCAmelCase    :




    def __init__(      self	:							List[Any]	,  a	:							str = None	,  a	:							list = []      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  0
       lowercase_						:     Dict							     =  choices
       lowercase_						:     List[str]							     =  prompt
       if sys.platform == "win32":
          lowercase_						:     List[Any]							     =  "*"
       else:
          lowercase_						:     Union[str, Any]							     =  "➔ "




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[int]	,  a	:							str = ""      ):






       '''simple docstring'''






       if sys.platform != "win32":
          writeColor(self.choices[index]	,  3_2	,  a      )
       else:
          forceWrite(self.choices[index]	,  a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]	,  a	:							int      ):






       '''simple docstring'''






       if index == self.position:
          forceWrite(f""" {self.arrow_char} """      )
          self.write_choice(a      )
       else:
          forceWrite(f"""    {self.choices[index]}"""      )
       reset_cursor()




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Direction	,  a	:							int = 1      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.position
       if direction == Direction.DOWN:
          if self.position + 1 >= len(self.choices      ):
             return
          self.position += num_spaces
       else:
          if self.position - 1 < 0:
             return
          self.position -= num_spaces
       clear_line()
       self.print_choice(a      )
       move_cursor(a	,  direction.name      )
       self.print_choice(self.position      )




    @input.mark(KEYMAP["up"]      )
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self.move_direction(Direction.UP      )




    @input.mark(KEYMAP["down"]      )
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       self.move_direction(Direction.DOWN      )




    @input.mark(KEYMAP["newline"]      )
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       return self.position




    @input.mark(KEYMAP["interrupt"]      )
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       raise KeyboardInterrupt




    @input.mark_multiple(*[KEYMAP[str(a      )] for number in range(1_0      )]      )
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     str							     =  int(chr(self.current_selection      )      )
       lowercase_						:     Tuple							     =  index - self.position
       if index == self.position:
          return
       if index < len(self.choices      ):
          if self.position > index:
             self.move_direction(Direction.UP	,  -movement      )
          elif self.position < index:
             self.move_direction(Direction.DOWN	,  a      )
          else:
             return
       else:
          return





    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							int = 0      ):






       '''simple docstring'''






       if self.prompt:
          linebreak()
          forceWrite(self.prompt	,  "\n"      )
          if in_colab:
             forceWrite("Please input a choice index (starting from 0), and press enter"	,  "\n"      )
          else:
             forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"	,  "\n"      )
       lowercase_						:     Dict							     =  default_choice
       for i in range(len(self.choices      )      ):
          self.print_choice(a      )
          forceWrite("\n"      )
       move_cursor(len(self.choices      ) - self.position	,  "UP"      )
       with cursor.hide():
          while True:
             if in_colab:
                try:
                   lowercase_						:     List[str]							     =  int(builtins.input()      )
                except ValueError:
                   lowercase_						:     List[Any]							     =  default_choice
             else:
                lowercase_						:     Dict							     =  self.handle_input()
             if choice is not None:
                reset_cursor()
                for _ in range(len(self.choices      ) + 1      ):
                   move_cursor(1	,  "UP"      )
                   clear_line()
                self.write_choice(a	,  "\n"      )
                return choice




