


'''simple docstring'''




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

UpperCAmelCase_      :		str          =		"tiny-wmt19-en-ru"

# Build

# borrowed from a test
UpperCAmelCase_      :		Any          =		[
    "l",
    "o",
    "w",
    "e",
    "r",
    "s",
    "t",
    "i",
    "d",
    "n",
    "w</w>",
    "r</w>",
    "t</w>",
    "lo",
    "low",
    "er</w>",
    "low</w>",
    "lowest</w>",
    "newer</w>",
    "wider</w>",
    "<unk>",
]
UpperCAmelCase_      :		List[str]          =		dict(zip(vocab, range(len(vocab))))
UpperCAmelCase_      :		List[Any]          =		["l o 123", "lo w 1456", "e r</w> 1789", ""]

with tempfile.TemporaryDirectory() as tmpdirname:
 UpperCAmelCase_      :		List[str]          =		Path(tmpdirname)
 UpperCAmelCase_      :		Optional[int]          =		build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
 UpperCAmelCase_      :		int          =		build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
 UpperCAmelCase_      :		Optional[Any]          =		build_dir / VOCAB_FILES_NAMES["merges_file"]
 with open(src_vocab_file, "w") as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, "w") as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, "w") as fp:
  fp.write("\n".join(merges))

 UpperCAmelCase_      :		Optional[Any]          =		FSMTTokenizer(
     langs=["en", "ru"],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

UpperCAmelCase_      :		Tuple          =		FSMTConfig(
    langs=["ru", "en"],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

UpperCAmelCase_      :		str          =		FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
UpperCAmelCase_      :		str          =		tokenizer(["Making tiny model"], return_tensors="pt")
UpperCAmelCase_      :		Union[str, Any]          =		tiny_model(**batch)

print("test output:", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru






'''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_      :		List[str]          =		logging.get_logger(__name__)
logging.set_verbosity_info()


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   if "xprophetnet" in prophetnet_checkpoint_path:
      _a					:  str						=  XLMProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Any						=  XLMProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )
   else:
      _a					:  Optional[Any]						=  ProphetNetForConditionalGenerationOld.from_pretrained(A      )
      _a		,						_a					:  Dict						=  ProphetNetForConditionalGeneration.from_pretrained(
          A		,	output_loading_info=A      )

   _a					:  Tuple						=  ['key_proj', 'value_proj', 'query_proj']

   _a					:  str						=  {
       '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					:  Tuple						=  key.split('.'      )

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

      _a					:  Dict						=  False
      for attribute in attributes:
         if attribute in mapping:
            _a					:  List[str]						=  mapping[attribute]
            if not hasattr(A		,	A      ) and len(A      ) > 0:
               _a					:  Any						=  attribute
         elif hasattr(A		,	A      ):
            _a					:  Dict						=  attribute

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

            elif attribute == "key_proj":
               _a					:  str						=  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
               _a					:  List[Any]						=  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
            elif attribute == "value_proj":
               _a					:  Optional[int]						=  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
               _a					:  str						=  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
            _a					:  int						=  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_1_2, "We want 512 position_embeddings."
            _a					:  Tuple						=  nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]      )
            _a					:  Any						=  True
            break

         if attribute.isdigit():
            _a					:  List[str]						=  model[int(A      )]
            _a					:  Union[str, Any]						=  old_model[int(A      )]
         else:
            _a					:  List[Any]						=  getattr(A		,	A      )

            if old_attribute == "":
               _a					:  List[str]						=  old_model
            else:
               if not hasattr(A		,	A      ):
                  raise ValueError(f'''{old_model} does not have {old_attribute}'''      )
               _a					:  Optional[int]						=  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_      :		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."
 )
 UpperCAmelCase_      :		List[str]          =		parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


