



"""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


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






def     _a (	UpperCAmelCase__     ,	UpperCAmelCase__     )						->    Dict:




      if "xprophetnet" in prophetnet_checkpoint_path:
            __SCREAMING_SNAKE_CASE       		=   XLMProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__     )
            __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   XLMProphetNetForConditionalGeneration.from_pretrained(
                lowerCamelCase__     ,	output_loading_info=lowerCamelCase__     )
      else:
            __SCREAMING_SNAKE_CASE       		=   ProphetNetForConditionalGenerationOld.from_pretrained(lowerCamelCase__     )
            __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   ProphetNetForConditionalGeneration.from_pretrained(
                lowerCamelCase__     ,	output_loading_info=lowerCamelCase__     )

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

      __SCREAMING_SNAKE_CASE       		=   {
          '''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"]:
            __SCREAMING_SNAKE_CASE       		=   key.split('''.'''     )

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

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

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

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

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

                        if old_attribute == "":
                              __SCREAMING_SNAKE_CASE       		=   old_model
                        else:
                              if not hasattr(lowerCamelCase__     ,	lowerCamelCase__     ):
                                    raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                              __SCREAMING_SNAKE_CASE       		=   getattr(lowerCamelCase__     ,	lowerCamelCase__     )

            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(lowerCamelCase__     )


if __name__ == "__main__":
       lowerCAmelCase__									=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."
       )
       lowerCAmelCase__									=parser.parse_args()
       convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		return " ".join(
		    """""".join(word[::-1]  ) if len(lowerCamelCase__  ) > 4 else word for word in sentence.split()  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			print(reverse_long_words("Hey wollef sroirraw"))




