




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


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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       if "xprophetnet" in prophetnet_checkpoint_path:
              lowerCamelCase   :      List[str]			 =					XLMProphetNetForConditionalGenerationOld.from_pretrained(_SCREAMING_SNAKE_CASE    )
              lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					XLMProphetNetForConditionalGeneration.from_pretrained(
                  _SCREAMING_SNAKE_CASE ,output_loading_info=_SCREAMING_SNAKE_CASE    )
       else:
              lowerCamelCase   :      Dict			 =					ProphetNetForConditionalGenerationOld.from_pretrained(_SCREAMING_SNAKE_CASE    )
              lowerCamelCase ,      lowerCamelCase   :      Dict			 =					ProphetNetForConditionalGeneration.from_pretrained(
                  _SCREAMING_SNAKE_CASE ,output_loading_info=_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      int			 =					["key_proj", "value_proj", "query_proj"]

       lowerCamelCase   :      Union[str, 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"]:
              lowerCamelCase   :      Union[str, Any]			 =					key.split("."    )

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

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

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

                            elif attribute == "key_proj":
                                   lowerCamelCase   :      int			 =					nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]    )
                                   lowerCamelCase   :      Dict			 =					nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]    )
                            elif attribute == "value_proj":
                                   lowerCamelCase   :      Dict			 =					nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]    )
                                   lowerCamelCase   :      Any			 =					nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]    )
                            lowerCamelCase   :      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] == 512, "We want 512 position_embeddings."
                            lowerCamelCase   :      str			 =					nn.Parameter(old_model.embed_positions.weight[:512, :]    )
                            lowerCamelCase   :      Union[str, Any]			 =					True
                            break

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

                            if old_attribute == "":
                                   lowerCamelCase   :      str			 =					old_model
                            else:
                                   if not hasattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    ):
                                          raise ValueError(f'''{old_model} does not have {old_attribute}'''    )
                                   lowerCamelCase   :      List[str]			 =					getattr(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

              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(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      List[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.'
       )
       SCREAMING_SNAKE_CASE__       :      Tuple  =   parser.parse_args()
       convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)












from __future__ import annotations

import requests

def      A      (      _SCREAMING_SNAKE_CASE    )	-> dict:
       lowerCamelCase   :      Tuple			 =					f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
       return requests.get(_SCREAMING_SNAKE_CASE    ).json()

def      A      (      _SCREAMING_SNAKE_CASE = 10    )	-> list[dict]:
       lowerCamelCase   :      str			 =					"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
       lowerCamelCase   :      Any			 =					requests.get(_SCREAMING_SNAKE_CASE    ).json()[:max_stories]
       return [get_hackernews_story(_SCREAMING_SNAKE_CASE    ) for story_id in story_ids]

def      A      (      _SCREAMING_SNAKE_CASE = 10    )	-> str:
       lowerCamelCase   :      str			 =					hackernews_top_stories(_SCREAMING_SNAKE_CASE    )
       return "\n".join("* [{title}]({url})".format(**_SCREAMING_SNAKE_CASE    ) for story in stories    )


if __name__ == "__main__":
       print(hackernews_top_stories_as_markdown())






