
"""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 					lowerCamelCase   (     _snake_case    ,_snake_case ):
    if "xprophetnet" in prophetnet_checkpoint_path:
        UpperCAmelCase__	:      str     							=					XLMProphetNetForConditionalGenerationOld.from_pretrained(_snake_case )
        UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					XLMProphetNetForConditionalGeneration.from_pretrained(
            _snake_case    ,output_loading_info=_snake_case )
    else:
        UpperCAmelCase__	:      List[str]     							=					ProphetNetForConditionalGenerationOld.from_pretrained(_snake_case )
        UpperCAmelCase__							,							UpperCAmelCase__	:      Tuple     							=					ProphetNetForConditionalGeneration.from_pretrained(
            _snake_case    ,output_loading_info=_snake_case )

    UpperCAmelCase__	:      Optional[int]     							=					['key_proj', 'value_proj', 'query_proj']

    UpperCAmelCase__	:      List[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"]:
        UpperCAmelCase__	:      Union[str, Any]     							=					key.split('.' )

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

        UpperCAmelCase__	:      Tuple     							=					False
        for attribute in attributes:
            if attribute in mapping:
                UpperCAmelCase__	:      List[Any]     							=					mapping[attribute]
                if not hasattr(_snake_case    ,_snake_case ) and len(_snake_case ) > 0:
                    UpperCAmelCase__	:      int     							=					attribute
            elif hasattr(_snake_case    ,_snake_case ):
                UpperCAmelCase__	:      List[Any]     							=					attribute

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

                elif attribute == "key_proj":
                    UpperCAmelCase__	:      Optional[Any]     							=					nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
                    UpperCAmelCase__	:      Any     							=					nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
                elif attribute == "value_proj":
                    UpperCAmelCase__	:      Optional[Any]     							=					nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
                    UpperCAmelCase__	:      Optional[Any]     							=					nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
                UpperCAmelCase__	:      List[str]     							=					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__	:      Optional[int]     							=					nn.Parameter(old_model.embed_positions.weight[:512, :] )
                UpperCAmelCase__	:      Union[str, Any]     							=					True
                break

            if attribute.isdigit():
                UpperCAmelCase__	:      Optional[Any]     							=					model[int(_snake_case )]
                UpperCAmelCase__	:      Dict     							=					old_model[int(_snake_case )]
            else:
                UpperCAmelCase__	:      Dict     							=					getattr(_snake_case    ,_snake_case )

                if old_attribute == "":
                    UpperCAmelCase__	:      Any     							=					old_model
                else:
                    if not hasattr(_snake_case    ,_snake_case ):
                        raise ValueError(F'''{old_model} does not have {old_attribute}''' )
                    UpperCAmelCase__	:      Optional[Any]     							=					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__":
    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)



"""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_      )

