'''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__     :	str				=			logging.get_logger(__name__)
logging.set_verbosity_info()







def __UpperCamelCase(   _A					:				str  ,			_A					:				str    ):
   '''simple docstring'''
   if "xprophetnet" in prophetnet_checkpoint_path:
      UpperCAmelCase__							:				List[str]   				=  XLMProphetNetForConditionalGenerationOld.from_pretrained(_A    )
      UpperCAmelCase__				,      UpperCAmelCase__							:				Union[str, Any]   				=  XLMProphetNetForConditionalGeneration.from_pretrained(
          _A  ,			output_loading_info=_A    )
   else:
      UpperCAmelCase__							:				Optional[Any]   				=  ProphetNetForConditionalGenerationOld.from_pretrained(_A    )
      UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  ProphetNetForConditionalGeneration.from_pretrained(
          _A  ,			output_loading_info=_A    )

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

   UpperCAmelCase__							:				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"]:
      UpperCAmelCase__							:				Tuple   				=  key.split('''.'''    )

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

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

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

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

         if attribute.isdigit():
            UpperCAmelCase__							:				Optional[Any]   				=  model[int(_A    )]
            UpperCAmelCase__							:				List[Any]   				=  old_model[int(_A    )]
         else:
            UpperCAmelCase__							:				Any   				=  getattr(_A  ,			_A    )

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





'''simple docstring'''

def __UpperCamelCase(   _A					:				str  ,			_A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  (
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   UpperCAmelCase__							:				list   				=  []
   for char_count in range(_A    ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]    )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]    )
   return "".join(_A    )


if __name__ == "__main__":
  print(alternative_string_arrange('AB', 'XYZ'), end=' ')




