



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


a_      :      Tuple    =     logging.get_logger(__name__)
logging.set_verbosity_info()






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 if "xprophetnet" in prophetnet_checkpoint_path:
  a__												= XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
  a__							,			a__												= XLMProphetNetForConditionalGeneration.from_pretrained(
      __UpperCAmelCase							,  output_loading_info=__UpperCAmelCase       )
 else:
  a__												= ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase       )
  a__							,			a__												= ProphetNetForConditionalGeneration.from_pretrained(
      __UpperCAmelCase							,  output_loading_info=__UpperCAmelCase       )

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

 a__												= {
     '''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__												= key.split('''.'''       )

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

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

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

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

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

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

  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(__UpperCAmelCase       )


if __name__ == "__main__":
     a_      :      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.'
     )
     a_      :      Optional[int]    =     parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)










import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


a_      :      int    =     '\\n    Text data.\n    Second line of data.'

a_      :      Optional[Any]    =     'file'






@pytest.fixture(scope='''session'''       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / (FILE_PATH + '''.zstd''')
 a__												= bytes(__UpperCAmelCase							,  '''utf-8'''       )
 with zstd.open(__UpperCAmelCase							,  '''wb'''       ) as f:
  f.write(__UpperCAmelCase       )
 return path






@pytest.fixture
def     __a					(		__UpperCAmelCase       ):
 with open(os.path.join(tmpfs.local_root_dir							,  __UpperCAmelCase       )							,  '''w'''       ) as f:
  f.write(__UpperCAmelCase       )
 return FILE_PATH






@pytest.mark.parametrize('''compression_format'''							,  ['''gzip''', '''xz''', '''zstd''']       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
 a__												= input_paths[compression_format]
 a__												= tmp_path / '''cache'''
 a__												= DownloadConfig(cache_dir=__UpperCAmelCase							,  extract_compressed_file=__UpperCAmelCase       )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''							,  [True, False]       )
@pytest.mark.parametrize('''default_cache_dir'''							,  [True, False]       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= '''custom_cache'''
 a__												= '''custom_extracted_dir'''
 a__												= tmp_path / '''custom_extracted_path'''
 if default_extracted:
  a__												= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
 else:
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''							,  __UpperCAmelCase       )
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''							,  str(__UpperCAmelCase       )       )
  a__												= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

 a__												= xz_file
 a__												= (
     DownloadConfig(extract_compressed_file=__UpperCAmelCase       )
     if default_cache_dir
     else DownloadConfig(cache_dir=tmp_path / custom_cache_dir							,  extract_compressed_file=__UpperCAmelCase       )
 )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 assert Path(__UpperCAmelCase       ).parent.parts[-2:] == expected






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(Path(__UpperCAmelCase       ).resolve()       )
 assert cached_path(__UpperCAmelCase       ) == text_file
 # relative path
 a__												= str(Path(__UpperCAmelCase       ).resolve().relative_to(Path(os.getcwd()       )       )       )
 assert cached_path(__UpperCAmelCase       ) == text_file






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(tmp_path.resolve() / '''__missing_file__.txt'''       )
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )
 # relative path
 a__												= '''./__missing_file__.txt'''
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase       ):
 a__												= get_from_cache(f"tmp://{tmpfs_file}"       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		):
 with pytest.raises(__UpperCAmelCase       ):
  cached_path('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  http_get('''https://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  http_head('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  ftp_get('''ftp://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  ftp_head('''ftp://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_get('''s3://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_head('''s3://huggingface.co'''       )





