
'''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_       :   Dict   =       logging.get_logger(__name__)
logging.set_verbosity_info()







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      if "xprophetnet" in prophetnet_checkpoint_path:
            _lowerCamelCase				:   Union[str, Any]								=   XLMProphetNetForConditionalGenerationOld.from_pretrained(lowercase_							)
            _lowerCamelCase				:   int								=   XLMProphetNetForConditionalGeneration.from_pretrained(
                lowercase_				,	output_loading_info=lowercase_							)
      else:
            _lowerCamelCase				:   int								=   ProphetNetForConditionalGenerationOld.from_pretrained(lowercase_							)
            _lowerCamelCase				:   Optional[int]								=   ProphetNetForConditionalGeneration.from_pretrained(
                lowercase_				,	output_loading_info=lowercase_							)

      _lowerCamelCase				:   Any								=   ["""key_proj""", """value_proj""", """query_proj"""]

      _lowerCamelCase				:   Optional[int]								=   {
          """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				:   Optional[int]								=   key.split("."							)

            if attributes[0] == "lm_head":
                  _lowerCamelCase				:   int								=   prophet
                  _lowerCamelCase				:   Tuple								=   prophet_old
            else:
                  _lowerCamelCase				:   Optional[int]								=   prophet.prophetnet
                  _lowerCamelCase				:   Optional[int]								=   prophet_old.model

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

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        _lowerCamelCase				:   Union[str, Any]								=   old_model.weight
                        logger.info(F'{attribute} is initialized.'							)
                        _lowerCamelCase				:   List[str]								=   True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        _lowerCamelCase				:   Tuple								=   old_model.bias
                        logger.info(F'{attribute} is initialized'							)
                        _lowerCamelCase				:   Optional[Any]								=   True
                        break
                  elif attribute in special_keys and hasattr(lowercase_				,	"in_proj_weight"							):
                        _lowerCamelCase				:   Union[str, Any]								=   old_model.in_proj_weight.shape[0] // 3
                        _lowerCamelCase				:   Dict								=   getattr(lowercase_				,	lowercase_							)
                        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				:   Tuple								=   nn.Parameter(old_model.in_proj_weight[:embed_dim, :]							)
                              _lowerCamelCase				:   Optional[Any]								=   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				:   List[Any]								=   nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]							)
                        elif attribute == "value_proj":
                              _lowerCamelCase				:   Union[str, Any]								=   nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]							)
                              _lowerCamelCase				:   Dict								=   nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]							)
                        _lowerCamelCase				:   Union[str, Any]								=   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				:   Union[str, Any]								=   nn.Parameter(old_model.embed_positions.weight[:512, :]							)
                        _lowerCamelCase				:   str								=   True
                        break

                  if attribute.isdigit():
                        _lowerCamelCase				:   Optional[Any]								=   model[int(lowercase_							)]
                        _lowerCamelCase				:   Optional[int]								=   old_model[int(lowercase_							)]
                  else:
                        _lowerCamelCase				:   List[Any]								=   getattr(lowercase_				,	lowercase_							)

                        if old_attribute == "":
                              _lowerCamelCase				:   Any								=   old_model
                        else:
                              if not hasattr(lowercase_				,	lowercase_							):
                                    raise ValueError(F'{old_model} does not have {old_attribute}'							)
                              _lowerCamelCase				:   str								=   getattr(lowercase_				,	lowercase_							)

            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(lowercase_							)


if __name__ == "__main__":
 UpperCAmelCase_       :   str   =       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_       :   Union[str, Any]   =       parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_       :   Tuple   =       {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
 from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
 import sys

 UpperCAmelCase_       :   Tuple   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)