
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							List[str]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							bool = True			,			lowerCAmelCase_     :							bool = False     ):

      """simple docstring"""
      _A:  Dict					=       scheduler
      _A:  int					=       optimizers if isinstance(lowerCAmelCase_			,			(list, tuple)     ) else [optimizers]
      _A:  Union[str, Any]					=       split_batches
      _A:  Dict					=       step_with_optimizer
      _A:  int					=       GradientState()


   def  __magic_name__				(					self     :							Any			,			*lowerCAmelCase_     :							int			,			**lowerCAmelCase_     :							Optional[Any]     ):

      """simple docstring"""
      if not self.step_with_optimizer:
         # No link between scheduler and optimizer -> just step
         self.scheduler.step(*lowerCAmelCase_			,			**lowerCAmelCase_     )
         return

      # Otherwise, first make sure the optimizer was stepped.
      if not self.gradient_state.sync_gradients:
         if self.gradient_state.adjust_scheduler:
            self.scheduler._step_count += 1
         return

      for opt in self.optimizers:
         if opt.step_was_skipped:
            return
      if self.split_batches:
         # Split batches -> the training dataloader batch size is not changed so one step per training step
         self.scheduler.step(*lowerCAmelCase_			,			**lowerCAmelCase_     )
      else:
         # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
         # num_processes steps per training step
         _A:  Optional[Any]					=       AcceleratorState().num_processes
         for _ in range(lowerCAmelCase_     ):
            # Special case when using OneCycle and `drop_last` was not used
            if hasattr(self.scheduler			,			'''total_steps'''     ):
               if self.scheduler._step_count <= self.scheduler.total_steps:
                  self.scheduler.step(*lowerCAmelCase_			,			**lowerCAmelCase_     )
            else:
               self.scheduler.step(*lowerCAmelCase_			,			**lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      return self.scheduler.get_last_lr()


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      return self.scheduler.state_dict()


   def  __magic_name__				(					self     :							Optional[int]			,			lowerCAmelCase_     :							Optional[int]     ):

      """simple docstring"""
      self.scheduler.load_state_dict(lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      return self.scheduler.get_lr()


   def  __magic_name__				(					self     :							Optional[int]			,			*lowerCAmelCase_     :							Tuple			,			**lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""
      return self.scheduler.print_lr(*lowerCAmelCase_			,			**lowerCAmelCase_     )





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__		:     List[Any]     			=      logging.get_logger(__name__)
logging.set_verbosity_info()






def   lowerCamelCase__							(					a		,		a      )      ->   Dict:
   if "xprophetnet" in prophetnet_checkpoint_path:
      _A:  List[Any]					=       XLMProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Union[str, Any]					=       XLMProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )
   else:
      _A:  Dict					=       ProphetNetForConditionalGenerationOld.from_pretrained(a      )
      _A			,  _A:  Tuple					=       ProphetNetForConditionalGeneration.from_pretrained(
          a		,		output_loading_info=a      )

   _A:  Optional[int]					=       ['''key_proj''', '''value_proj''', '''query_proj''']

   _A:  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"]:
      _A:  List[str]					=       key.split('''.'''      )

      if attributes[0] == "lm_head":
         _A:  Optional[int]					=       prophet
         _A:  Tuple					=       prophet_old
      else:
         _A:  Tuple					=       prophet.prophetnet
         _A:  Any					=       prophet_old.model

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

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

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

         if attribute.isdigit():
            _A:  Tuple					=       model[int(a      )]
            _A:  int					=       old_model[int(a      )]
         else:
            _A:  Union[str, Any]					=       getattr(a		,		a      )

            if old_attribute == "":
               _A:  Union[str, Any]					=       old_model
            else:
               if not hasattr(a		,		a      ):
                  raise ValueError(f"""{old_model} does not have {old_attribute}"""      )
               _A:  List[Any]					=       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__":
   UpperCAmelCase__		:     Dict     			=      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__		:     Tuple     			=      parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)



