




"""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 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: str      ):
     """simple docstring"""

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

     lowerCAmelCase__          =			["key_proj", "value_proj", "query_proj"]

     lowerCAmelCase__          =			{
         "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__          =			key.split("."      )

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

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

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

                    elif attribute == "key_proj":
                         lowerCAmelCase__          =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
                         lowerCAmelCase__          =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
                    elif attribute == "value_proj":
                         lowerCAmelCase__          =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
                         lowerCAmelCase__          =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
                    lowerCAmelCase__          =			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__          =			nn.Parameter(old_model.embed_positions.weight[:512, :]      )
                    lowerCAmelCase__          =			True
                    break

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

                    if old_attribute == "":
                         lowerCAmelCase__          =			old_model
                    else:
                         if not hasattr(lowerCAmelCase_       ,    lowerCAmelCase_      ):
                              raise ValueError(F'{old_model} does not have {old_attribute}'      )
                         lowerCAmelCase__          =			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__":
     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)






import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


UpperCamelCase								=					'__DUMMY_TRANSFORMERS_USER__'
UpperCamelCase								=					'Dummy User'
UpperCamelCase								=					'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'

UpperCamelCase								=					'https://hub-ci.huggingface.co'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
UpperCamelCase								=					CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
UpperCamelCase								=					Path('~/.huggingface/hub_ci_token').expanduser()



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     monkeypatch.setattr(
         "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     monkeypatch.setattr("datasets.config.HF_ENDPOINT"       ,    lowerCAmelCase_      )
     monkeypatch.setattr("datasets.config.HUB_DATASETS_URL"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token"       ,    lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[Any]      ):
     """simple docstring"""

     HfFolder.save_token(lowerCAmelCase_      )
     yield
     HfFolder.delete_token()



@pytest.fixture(scope="session"      )
def 							_A       (						):
     """simple docstring"""

     return HfApi(endpoint=lowerCAmelCase_      )



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi      ):
     """simple docstring"""

     lowerCAmelCase__          =			HfFolder.get_token()
     HfFolder.save_token(lowerCAmelCase_      )
     yield CI_HUB_USER_TOKEN
     if previous_token is not None:
          HfFolder.save_token(lowerCAmelCase_      )



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     def _cleanup_repo(lowerCAmelCase_					: Optional[Any]      ):
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )

     return _cleanup_repo



@pytest.fixture
def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     @contextmanager
     def _temporary_repo(lowerCAmelCase_					: str      ):
          try:
               yield repo_id
          finally:
               cleanup_repo(lowerCAmelCase_      )

     return _temporary_repo



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Union[str, Any]       ,    lowerCAmelCase_					: Any      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data/text_data.txt"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     return hf_private_dataset_repo_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_txt_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass



@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Tuple      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_txt_data_



@pytest.fixture(scope="session"      )
def 							_A       (						lowerCAmelCase_					: HfApi       ,    lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'repo_zipped_img_data-{int(time.time() * 1_0E3      )}'
     lowerCAmelCase__          =			F'{CI_HUB_USER}/{repo_name}'
     hf_api.create_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"       ,    private=lowerCAmelCase_      )
     hf_api.upload_file(
         token=lowerCAmelCase_       ,    path_or_fileobj=str(lowerCAmelCase_      )       ,    path_in_repo="data.zip"       ,    repo_id=lowerCAmelCase_       ,    repo_type="dataset"       ,    )
     yield repo_id
     try:
          hf_api.delete_repo(lowerCAmelCase_       ,    token=lowerCAmelCase_       ,    repo_type="dataset"      )
     except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
          pass






@pytest.fixture()
def 							_A       (						lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     return hf_private_dataset_repo_zipped_img_data_
