






'''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


__lowerCAmelCase       		=      logging.get_logger(__name__)

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

__lowerCAmelCase       		=      {
    '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'
        ),
    }
}

__lowerCAmelCase       		=      {
    'junnyu/roformer_chinese_small': 1_536,
    'junnyu/roformer_chinese_base': 1_536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


__lowerCAmelCase       		=      {
    '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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				VOCAB_FILES_NAMES
			snake_case__  =				PRETRAINED_VOCAB_FILES_MAP
			snake_case__  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case__  =				PRETRAINED_INIT_CONFIGURATION
			snake_case__  =				RoFormerTokenizer






			def __init__( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=None  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]=None  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=True  ,						__SCREAMING_SNAKE_CASE      :			List[str]="[UNK]"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="[SEP]"  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]="[PAD]"  ,						__SCREAMING_SNAKE_CASE      :			List[str]="[CLS]"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]="[MASK]"  ,						__SCREAMING_SNAKE_CASE      :			str=True  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=None  ,						**__SCREAMING_SNAKE_CASE      :			Dict  ,						)			->			Dict:
							super().__init__(
							    __SCREAMING_SNAKE_CASE  ,						tokenizer_file=__SCREAMING_SNAKE_CASE  ,						do_lower_case=__SCREAMING_SNAKE_CASE  ,						unk_token=__SCREAMING_SNAKE_CASE  ,						sep_token=__SCREAMING_SNAKE_CASE  ,						pad_token=__SCREAMING_SNAKE_CASE  ,						cls_token=__SCREAMING_SNAKE_CASE  ,						mask_token=__SCREAMING_SNAKE_CASE  ,						tokenize_chinese_chars=__SCREAMING_SNAKE_CASE  ,						strip_accents=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)

							a_		:  Any											=      json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
							if (
							    pre_tok_state.get('''lowercase'''  ,						__SCREAMING_SNAKE_CASE					) != do_lower_case
							    or pre_tok_state.get('''strip_accents'''  ,						__SCREAMING_SNAKE_CASE					) != strip_accents
							):
											a_		:  Optional[int]											=      getattr(__SCREAMING_SNAKE_CASE  ,						pre_tok_state.pop('''type'''					)					)
											a_		:  Tuple											=      do_lower_case
											a_		:  int											=      strip_accents
											a_		:  List[str]											=      pre_tok_class(**__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      do_lower_case






			def __getstate__( self      :			str					)			->			Tuple:
							a_		:  List[Any]											=      self.__dict__.copy()
							a_		:  Tuple											=      BertPreTokenizer()
							return state






			def __setstate__( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]					)			->			List[Any]:
							a_		:  int											=      d
							a_		:  Optional[Any]											=      self.__dict__['''_tokenizer'''].get_vocab()
							a_		:  Union[str, Any]											=      PreTokenizer.custom(JiebaPreTokenizer(__SCREAMING_SNAKE_CASE					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=None					)			->			Union[str, Any]:
							a_		:  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 			SCREAMING_SNAKE_CASE      ( self      :			int  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  List[str]											=      [self.sep_token_id]
							a_		:  str											=      [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 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							a_		:  List[str]											=      self._tokenizer.model.save(__SCREAMING_SNAKE_CASE  ,						name=__SCREAMING_SNAKE_CASE					)
							return tuple(__SCREAMING_SNAKE_CASE					)







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			str=None  ,						__SCREAMING_SNAKE_CASE      :			Dict=None  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=False  ,						**__SCREAMING_SNAKE_CASE      :			Any  ,						)			->			str:
							a_		:  Any											=      BertPreTokenizer()
							return super().save_pretrained(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE					)












'''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


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





def 		_UpperCAmelCase   (							__A							:   str						,		__A							:   str      ):
				if "xprophetnet" in prophetnet_checkpoint_path:
								a_		:  Tuple											=      XLMProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Optional[Any]											=      XLMProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )
				else:
								a_		:  List[Any]											=      ProphetNetForConditionalGenerationOld.from_pretrained(__A      )
								a_				,			a_		:  Any											=      ProphetNetForConditionalGeneration.from_pretrained(
								    __A						,		output_loading_info=__A      )

				a_		:  str											=      ['''key_proj''', '''value_proj''', '''query_proj''']

				a_		:  Tuple											=      {
				    '''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_		:  List[str]											=      key.split('''.'''      )

								if attributes[0] == "lm_head":
												a_		:  List[str]											=      prophet
												a_		:  Dict											=      prophet_old
								else:
												a_		:  str											=      prophet.prophetnet
												a_		:  int											=      prophet_old.model

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

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

																elif attribute == "key_proj":
																				a_		:  List[Any]											=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]      )
																				a_		:  Optional[int]											=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]      )
																elif attribute == "value_proj":
																				a_		:  Tuple											=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]      )
																				a_		:  Any											=      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]      )
																a_		:  Dict											=      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_12, "We want 512 position_embeddings."
																a_		:  Union[str, Any]											=      nn.Parameter(old_model.embed_positions.weight[:5_12, :]      )
																a_		:  Optional[Any]											=      True
																break

												if attribute.isdigit():
																a_		:  Union[str, Any]											=      model[int(__A      )]
																a_		:  str											=      old_model[int(__A      )]
												else:
																a_		:  Tuple											=      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[Any]											=      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__":
		__lowerCAmelCase       		=      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.'
		)
		__lowerCAmelCase       		=      parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




