
'''simple docstring'''





import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
						import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.txt"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt",
    },
}

UpperCAmelCase    					=   {
    "openbmb/cpm-ant-10b": 1_024,
}

def      A (       A_							:					Union[str, Any]       ):
						snake_case       :     int =    collections.OrderedDict()
						with open(A_    ,							'''r'''    ,							encoding='''utf-8'''       ) as reader:
												snake_case       :     Union[str, Any] =    reader.readlines()
						for index, token in enumerate(A_       ):
												snake_case       :     Any =    token.rstrip('''\n'''       )
												snake_case       :     Optional[int] =    index
						return vocab

class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				List[str]="<unk>",    SCREAMING_SNAKE_CASE_	:				Dict=2_00      ):
										snake_case       :     Any =    vocab
										snake_case       :     Optional[int] =    unk_token
										snake_case       :     Dict =    max_input_chars_per_word




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
										snake_case       :     str =    list(SCREAMING_SNAKE_CASE_      )
										if len(SCREAMING_SNAKE_CASE_      ) > self.max_input_chars_per_word:
																return [self.unk_token]

										snake_case       :     str =    0
										snake_case       :     Optional[int] =    []
										while start < len(SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Union[str, Any] =    len(SCREAMING_SNAKE_CASE_      )
																snake_case       :     Any =    None
																while start < end:
																						snake_case       :     Optional[int] =    ''''''.join(chars[start:end]      )
																						if substr in self.vocab:
																												snake_case       :     Dict =    substr
																												break
																						end -= 1
																if cur_substr is None:
																						sub_tokens.append(self.unk_token      )
																						start += 1
																else:
																						sub_tokens.append(SCREAMING_SNAKE_CASE_      )
																						snake_case       :     Any =    end

										return sub_tokens




class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = False




				def __init__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[Any]="<d>",    SCREAMING_SNAKE_CASE_	:				int="</d>",    SCREAMING_SNAKE_CASE_	:				Tuple="<s>",    SCREAMING_SNAKE_CASE_	:				Optional[Any]="</s>",    SCREAMING_SNAKE_CASE_	:				List[Any]="<pad>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<unk>",    SCREAMING_SNAKE_CASE_	:				Dict="</n>",    SCREAMING_SNAKE_CASE_	:				List[str]="</_>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="left",    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										requires_backends(self,    ['''jieba''']      )
										super().__init__(
										    bod_token=SCREAMING_SNAKE_CASE_,    eod_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    pad_token=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    line_token=SCREAMING_SNAKE_CASE_,    space_token=SCREAMING_SNAKE_CASE_,    padding_side=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										snake_case       :     Optional[Any] =    bod_token
										snake_case       :     int =    eod_token
										snake_case       :     Dict =    load_vocab(SCREAMING_SNAKE_CASE_      )
										snake_case       :     Any =    self.encoder[space_token]
										snake_case       :     List[str] =    self.encoder[line_token]

										del self.encoder[space_token]
										del self.encoder[line_token]

										snake_case       :     Tuple =    collections.OrderedDict(sorted(self.encoder.items(),    key=lambda SCREAMING_SNAKE_CASE_      : x[1]      )      )
										snake_case       :     Optional[Any] =    {v: k for k, v in self.encoder.items()}

										snake_case       :     Optional[Any] =    WordpieceTokenizer(vocab=self.encoder,    unk_token=self.unk_token      )




				@property
				def 	__snake_case							(							self	:				Union[str, Any]      ):
										return self.encoder[self.bod_token]




				@property
				def 	__snake_case							(							self	:				str      ):
										return self.encoder[self.eod_token]




				@property
				def 	__snake_case							(							self	:				str      ):
										return self.encoder["\n"]




				@property
				def 	__snake_case							(							self	:				Tuple      ):
										return len(self.encoder      )




				def 	__snake_case							(							self	:				List[Any]      ):
										return dict(self.encoder,    **self.added_tokens_encoder      )




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     Tuple =    []
										for x in jieba.cut(SCREAMING_SNAKE_CASE_,    cut_all=SCREAMING_SNAKE_CASE_      ):
																output_tokens.extend(self.wordpiece_tokenizer.tokenize(SCREAMING_SNAKE_CASE_      )      )
										return output_tokens




				def 	__snake_case							(							self	:				List[str],    SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     List[Any] =    [i for i in token_ids if i >= 0]
										snake_case       :     Any =    [
										    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
										]
										return super()._decode(SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										return token in self.encoder




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										return "".join(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				List[str]      ):
										return self.encoder.get(SCREAMING_SNAKE_CASE_,    self.encoder.get(self.unk_token      )      )




				def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										return self.decoder.get(SCREAMING_SNAKE_CASE_,    self.unk_token      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										if os.path.isdir(SCREAMING_SNAKE_CASE_      ):
																snake_case       :     Tuple =    os.path.join(
																    SCREAMING_SNAKE_CASE_,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
										else:
																snake_case       :     List[str] =    (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
										snake_case       :     Dict =    0
										if " " in self.encoder:
																snake_case       :     Optional[int] =    self.encoder[''' ''']
																del self.encoder[" "]
										if "\n" in self.encoder:
																snake_case       :     Union[str, Any] =    self.encoder['''\n''']
																del self.encoder["\n"]
										snake_case       :     Tuple =    collections.OrderedDict(sorted(self.encoder.items(),    key=lambda SCREAMING_SNAKE_CASE_      : x[1]      )      )
										with open(SCREAMING_SNAKE_CASE_,    '''w''',    encoding='''utf-8'''      ) as writer:
																for token, token_index in self.encoder.items():
																						if index != token_index:
																												logger.warning(
																												    F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																												    ''' Please check that the vocabulary is not corrupted!'''      )
																												snake_case       :     Any =    token_index
																						writer.write(token + '''\n'''      )
																						index += 1
										return (vocab_file,)




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				List[int] = None      ):
										if token_ids_a is None:
																return [self.bos_token_id] + token_ids_a
										return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[int],    SCREAMING_SNAKE_CASE_	:				Optional[List[int]] = None,    SCREAMING_SNAKE_CASE_	:				bool = False      ):

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,    token_ids_a=SCREAMING_SNAKE_CASE_,    already_has_special_tokens=SCREAMING_SNAKE_CASE_      )

										if token_ids_a is not None:
																return [1] + ([0] * len(SCREAMING_SNAKE_CASE_      )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_      ))
										return [1] + ([0] * len(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


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

def      A (       A_							:					str    ,							A_							:					str       ):
						if "xprophetnet" in prophetnet_checkpoint_path:
												snake_case       :     Any =    XLMProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     Tuple =    XLMProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )
						else:
												snake_case       :     Union[str, Any] =    ProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     List[Any] =    ProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )

						snake_case       :     Union[str, Any] =    ['''key_proj''', '''value_proj''', '''query_proj''']

						snake_case       :     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"]:
												snake_case       :     Optional[Any] =    key.split('''.'''       )

												if attributes[0] == "lm_head":
																		snake_case       :     Optional[int] =    prophet
																		snake_case       :     Union[str, Any] =    prophet_old
												else:
																		snake_case       :     Optional[int] =    prophet.prophetnet
																		snake_case       :     Any =    prophet_old.model

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

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

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

																		if attribute.isdigit():
																								snake_case       :     Optional[Any] =    model[int(A_       )]
																								snake_case       :     List[str] =    old_model[int(A_       )]
																		else:
																								snake_case       :     Optional[Any] =    getattr(A_    ,							A_       )

																								if old_attribute == "":
																														snake_case       :     Union[str, Any] =    old_model
																								else:
																														if not hasattr(A_    ,							A_       ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																														snake_case       :     Tuple =    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    					=   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)

