






'''simple docstring'''







import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       		=      logging.get_logger(__name__)


__lowerCAmelCase       		=      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

__lowerCAmelCase       		=      {
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

__lowerCAmelCase       		=      {
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}





@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		_UpperCAmelCase   (							):
				a_		:  int											=      (
				    list(range(ord('''!'''      )						,		ord('''~'''      ) + 1      )      ) + list(range(ord('''¡'''      )						,		ord('''¬'''      ) + 1      )      ) + list(range(ord('''®'''      )						,		ord('''ÿ'''      ) + 1      )      )
				)
				a_		:  List[str]											=      bs[:]
				a_		:  Optional[int]											=      0
				for b in range(2**8      ):
								if b not in bs:
												bs.append(__A      )
												cs.append(2**8 + n      )
												n += 1
				a_		:  str											=      [chr(__A      ) for n in cs]
				return dict(zip(__A						,		__A      )      )





def 		_UpperCAmelCase   (							__A							:   Optional[int]      ):
				a_		:  int											=      set()
				a_		:  Tuple											=      word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)      )
								a_		:  Optional[int]											=      char
				return pairs





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__  =				["input_ids", "attention_mask"]






			def __init__( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			Any  ,						__SCREAMING_SNAKE_CASE      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]="replace"  ,						__SCREAMING_SNAKE_CASE      :			Any="<s>"  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]="</s>"  ,						__SCREAMING_SNAKE_CASE      :			int="</s>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]="<s>"  ,						__SCREAMING_SNAKE_CASE      :			int="<unk>"  ,						__SCREAMING_SNAKE_CASE      :			int="<pad>"  ,						__SCREAMING_SNAKE_CASE      :			int="<mask>"  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]=False  ,						**__SCREAMING_SNAKE_CASE      :			Union[str, Any]  ,						)			->			Any:
							a_		:  Dict											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else bos_token
							a_		:  Any											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else eos_token
							a_		:  Optional[int]											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else sep_token
							a_		:  Union[str, Any]											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else cls_token
							a_		:  Tuple											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else unk_token
							a_		:  Any											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							a_		:  str											=      AddedToken(__SCREAMING_SNAKE_CASE  ,						lstrip=__SCREAMING_SNAKE_CASE  ,						rstrip=__SCREAMING_SNAKE_CASE					) if isinstance(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					) else mask_token

							super().__init__(
							    errors=__SCREAMING_SNAKE_CASE  ,						bos_token=__SCREAMING_SNAKE_CASE  ,						eos_token=__SCREAMING_SNAKE_CASE  ,						unk_token=__SCREAMING_SNAKE_CASE  ,						sep_token=__SCREAMING_SNAKE_CASE  ,						cls_token=__SCREAMING_SNAKE_CASE  ,						pad_token=__SCREAMING_SNAKE_CASE  ,						mask_token=__SCREAMING_SNAKE_CASE  ,						add_prefix_space=__SCREAMING_SNAKE_CASE  ,						**__SCREAMING_SNAKE_CASE  ,						)

							with open(__SCREAMING_SNAKE_CASE  ,						encoding='''utf-8'''					) as vocab_handle:
											a_		:  Optional[Any]											=      json.load(__SCREAMING_SNAKE_CASE					)
							a_		:  str											=      {v: k for k, v in self.encoder.items()}
							a_		:  Union[str, Any]											=      errors  # how to handle errors in decoding
							a_		:  int											=      bytes_to_unicode()
							a_		:  int											=      {v: k for k, v in self.byte_encoder.items()}
							with open(__SCREAMING_SNAKE_CASE  ,						encoding='''utf-8'''					) as merges_handle:
											a_		:  Optional[int]											=      merges_handle.read().split('''\n'''					)[1:-1]
							a_		:  Dict											=      [tuple(merge.split()					) for merge in bpe_merges]
							a_		:  Dict											=      dict(zip(__SCREAMING_SNAKE_CASE  ,						range(len(__SCREAMING_SNAKE_CASE					)					)					)					)
							a_		:  Dict											=      {}
							a_		:  Optional[Any]											=      add_prefix_space

							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
							a_		:  str											=      re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''					)






			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			Optional[int]:
							return len(self.encoder					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			List[str]:
							return dict(self.encoder  ,						**self.added_tokens_encoder					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			List[str]					)			->			Tuple:
							if token in self.cache:
											return self.cache[token]
							a_		:  Tuple											=      tuple(__SCREAMING_SNAKE_CASE					)
							a_		:  Dict											=      get_pairs(__SCREAMING_SNAKE_CASE					)

							if not pairs:
											return token

							while True:
											a_		:  Dict											=      min(__SCREAMING_SNAKE_CASE  ,						key=lambda __SCREAMING_SNAKE_CASE					: self.bpe_ranks.get(__SCREAMING_SNAKE_CASE  ,						float('''inf'''					)					)					)
											if bigram not in self.bpe_ranks:
															break
											a_				,			a_		:  Optional[Any]											=      bigram
											a_		:  List[Any]											=      []
											a_		:  Union[str, Any]											=      0
											while i < len(__SCREAMING_SNAKE_CASE					):
															try:
																			a_		:  Optional[Any]											=      word.index(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE					)
															except ValueError:
																			new_word.extend(word[i:]					)
																			break
															else:
																			new_word.extend(word[i:j]					)
																			a_		:  List[Any]											=      j

															if word[i] == first and i < len(__SCREAMING_SNAKE_CASE					) - 1 and word[i + 1] == second:
																			new_word.append(first + second					)
																			i += 2
															else:
																			new_word.append(word[i]					)
																			i += 1
											a_		:  List[Any]											=      tuple(__SCREAMING_SNAKE_CASE					)
											a_		:  Dict											=      new_word
											if len(__SCREAMING_SNAKE_CASE					) == 1:
															break
											else:
															a_		:  Union[str, Any]											=      get_pairs(__SCREAMING_SNAKE_CASE					)
							a_		:  str											=      ''' '''.join(__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      word
							return word






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			str					)			->			int:
							a_		:  int											=      []
							for token in re.findall(self.pat  ,						__SCREAMING_SNAKE_CASE					):
											a_		:  Dict											=      ''''''.join(
											    self.byte_encoder[b] for b in token.encode('''utf-8'''					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__SCREAMING_SNAKE_CASE					).split(''' '''					)					)
							return bpe_tokens






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Tuple					)			->			Any:
							return self.encoder.get(__SCREAMING_SNAKE_CASE  ,						self.encoder.get(self.unk_token					)					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]					)			->			Optional[int]:
							return self.decoder.get(__SCREAMING_SNAKE_CASE					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[int]  ,						__SCREAMING_SNAKE_CASE      :			Union[str, Any]					)			->			Optional[Any]:
							a_		:  Tuple											=      ''''''.join(__SCREAMING_SNAKE_CASE					)
							a_		:  List[Any]											=      bytearray([self.byte_decoder[c] for c in text]					).decode('''utf-8'''  ,						errors=self.errors					)
							return text






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			str  ,						__SCREAMING_SNAKE_CASE      :			Optional[str] = None					)			->			Tuple[str]:
							if not os.path.isdir(__SCREAMING_SNAKE_CASE					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
							a_		:  Optional[int]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
							a_		:  Optional[int]											=      os.path.join(
							    __SCREAMING_SNAKE_CASE  ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']					)

							with open(__SCREAMING_SNAKE_CASE  ,						'''w'''  ,						encoding='''utf-8'''					) as f:
											f.write(json.dumps(self.encoder  ,						indent=2  ,						sort_keys=__SCREAMING_SNAKE_CASE  ,						ensure_ascii=__SCREAMING_SNAKE_CASE					) + '''\n'''					)

							a_		:  int											=      0
							with open(__SCREAMING_SNAKE_CASE  ,						'''w'''  ,						encoding='''utf-8'''					) as writer:
											writer.write('''#version: 0.2\n'''					)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,						key=lambda __SCREAMING_SNAKE_CASE					: kv[1]					):
															if index != token_index:
																			logger.warning(
																			    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																			    ''' Please check that the tokenizer is not corrupted!'''					)
																			a_		:  Tuple											=      token_index
															writer.write(''' '''.join(__SCREAMING_SNAKE_CASE					) + '''\n'''					)
															index += 1

							return vocab_file, merge_file






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							a_		:  Optional[Any]											=      [self.cls_token_id]
							a_		:  Tuple											=      [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None  ,						__SCREAMING_SNAKE_CASE      :			bool = False					)			->			List[int]:
							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 None:
											return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]
							return [1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE					)) + [1]






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]  ,						__SCREAMING_SNAKE_CASE      :			List[int]  ,						__SCREAMING_SNAKE_CASE      :			Optional[List[int]] = None					)			->			List[int]:
							a_		:  Union[str, Any]											=      [self.sep_token_id]
							a_		:  List[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 + sep + token_ids_a + sep					) * [0]







			def 			SCREAMING_SNAKE_CASE      ( self      :			Any  ,						__SCREAMING_SNAKE_CASE      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			Any=False  ,						**__SCREAMING_SNAKE_CASE      :			List[str]					)			->			Optional[Any]:
							a_		:  Dict											=      kwargs.pop('''add_prefix_space'''  ,						self.add_prefix_space					)
							if (is_split_into_words or add_prefix_space) and (len(__SCREAMING_SNAKE_CASE					) > 0 and not text[0].isspace()):
											a_		:  Any											=      ''' ''' + text
							return (text, kwargs)












'''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)




