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 		=      logging.get_logger(__name__)
logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 if "xprophetnet" in prophetnet_checkpoint_path:
  UpperCamelCase			  =      XLMProphetNetForConditionalGenerationOld.from_pretrained(_snake_case       )
  UpperCamelCase			  =      XLMProphetNetForConditionalGeneration.from_pretrained(
      _snake_case							,					output_loading_info=_snake_case       )
 else:
  UpperCamelCase			  =      ProphetNetForConditionalGenerationOld.from_pretrained(_snake_case       )
  UpperCamelCase			  =      ProphetNetForConditionalGeneration.from_pretrained(
      _snake_case							,					output_loading_info=_snake_case       )

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

 UpperCamelCase			  =      {
     '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"]:
  UpperCamelCase			  =      key.split("""."""       )

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

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

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

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

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

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


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      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 		=      parser.parse_args()
     convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCamelCase__          =	logging.get_logger(__name__)

UpperCamelCase__          =	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          =	{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': 15_36,
    'junnyu/roformer_chinese_base': 15_36,
    'junnyu/roformer_chinese_char_small': 5_12,
    'junnyu/roformer_chinese_char_base': 5_12,
    'junnyu/roformer_small_discriminator': 1_28,
    'junnyu/roformer_small_generator': 1_28,
}


UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   int           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   Dict           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Union[str, Any]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   Tuple           =     PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase							:   Optional[int]           =     RoFormerTokenizer



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	UpperCamelCase_="[UNK]"     ,	UpperCamelCase_="[SEP]"     ,	UpperCamelCase_="[PAD]"     ,	UpperCamelCase_="[CLS]"     ,	UpperCamelCase_="[MASK]"     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	**UpperCamelCase_     ,	):
          super().__init__(
              UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	do_lower_case=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	sep_token=UpperCamelCase_     ,	pad_token=UpperCamelCase_     ,	cls_token=UpperCamelCase_     ,	mask_token=UpperCamelCase_     ,	tokenize_chinese_chars=UpperCamelCase_     ,	strip_accents=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              pre_tok_state.get('lowercase'     ,	UpperCamelCase_      ) != do_lower_case
              or pre_tok_state.get('strip_accents'     ,	UpperCamelCase_      ) != strip_accents
          ):
              UpperCAmelCase__	:      Any     							=					getattr(UpperCamelCase_     ,	pre_tok_state.pop('type'      )      )
              UpperCAmelCase__	:      str     							=					do_lower_case
              UpperCAmelCase__	:      Union[str, Any]     							=					strip_accents
              UpperCAmelCase__	:      Dict     							=					pre_tok_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					do_lower_case



      def __getstate__(   self      ):
          UpperCAmelCase__	:      int     							=					self.__dict__.copy()
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return state



      def __setstate__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					d
          UpperCAmelCase__	:      List[str]     							=					self.__dict__['_tokenizer'].get_vocab()
          UpperCAmelCase__	:      List[Any]     							=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase_      )      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          UpperCAmelCase__	:      List[Any]     							=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      int     							=					[self.sep_token_id]
          UpperCAmelCase__	:      Dict     							=					[self.cls_token_id]
          if token_ids_a is None:
              return len(cls + token_ids_a + sep      ) * [0]
          return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      Any     							=					self._tokenizer.model.save(UpperCamelCase_     ,	name=UpperCamelCase_      )
          return tuple(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=False     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return super().save_pretrained(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )

