




import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE:     List[str]									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= '''▁'''

SCREAMING_SNAKE_CASE:     Tuple									= {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

SCREAMING_SNAKE_CASE:     List[Any]									= {
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

SCREAMING_SNAKE_CASE:     Tuple									= {
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

SCREAMING_SNAKE_CASE:     List[Any]									= {
    '''ernie-m-base''': 5_1_4,
    '''ernie-m-large''': 5_1_4,
}

SCREAMING_SNAKE_CASE:     Optional[Any]									= {
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =["input_ids"]

						lowerCAmelCase__			      =VOCAB_FILES_NAMES
						lowerCAmelCase__			      =PRETRAINED_INIT_CONFIGURATION
						lowerCAmelCase__			      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__			      =PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__			      =RESOURCE_FILES_NAMES



						def __init__(			self	:   List[Any]					,							snake_case__	:   Optional[Any]					,							snake_case__	:   int=None					,							snake_case__	:   int=False					,							snake_case__	:   List[Any]="utf8"					,							snake_case__	:   Dict="[UNK]"					,							snake_case__	:   Any="[SEP]"					,							snake_case__	:   Dict="[PAD]"					,							snake_case__	:   Union[str, Any]="[CLS]"					,							snake_case__	:   Optional[Any]="[MASK]"					,							snake_case__	:   Optional[Dict[str, Any]] = None					,							**snake_case__	:   Any					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{} if sp_model_kwargs is None else sp_model_kwargs
								super().__init__(
								    do_lower_case=snake_case__					,							unk_token=snake_case__					,							sep_token=snake_case__					,							pad_token=snake_case__					,							cls_token=snake_case__					,							mask_token=snake_case__					,							vocab_file=snake_case__					,							encoding=snake_case__					,							sp_model_kwargs=self.sp_model_kwargs					,							**snake_case__					,							)
								SCREAMING_SNAKE_CASE_					      =							do_lower_case
								SCREAMING_SNAKE_CASE_					      =							sentencepiece_model_ckpt
								SCREAMING_SNAKE_CASE_					      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(snake_case__  )

								# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
								if vocab_file is not None:
										SCREAMING_SNAKE_CASE_					      =							self.load_vocab(filepath=snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							{self.sp_model.id_to_piece(snake_case__  ): id for id in range(self.sp_model.get_piece_size()  )}
								SCREAMING_SNAKE_CASE_					      =							{v: k for k, v in self.vocab.items()}



						def 	__a					(			self	:   Dict					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								if text is None:
										return None

								SCREAMING_SNAKE_CASE_					      =							self.tokenize(snake_case__  )
								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							'', []

								for i, ch in enumerate(snake_case__  ):
										if ch in self.SP_CHAR_MAPPING:
												SCREAMING_SNAKE_CASE_					      =							self.SP_CHAR_MAPPING.get(snake_case__  )
										else:
												SCREAMING_SNAKE_CASE_					      =							unicodedata.normalize('NFKC'					,							snake_case__  )
										if self.is_whitespace(snake_case__  ):
												continue
										normalized_text += ch
										char_mapping.extend([i] * len(snake_case__  )  )

								SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							normalized_text, [], 0

								if self.do_lower_case:
										SCREAMING_SNAKE_CASE_					      =							text.lower()

								for token in split_tokens:
										if token[:1] == "▁":
												SCREAMING_SNAKE_CASE_					      =							token[1:]
										SCREAMING_SNAKE_CASE_					      =							text[offset:].index(snake_case__  ) + offset
										SCREAMING_SNAKE_CASE_					      =							start + len(snake_case__  )

										token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)  )
										SCREAMING_SNAKE_CASE_					      =							end
								return token_mapping



						@property
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								return len(self.vocab  )



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								return dict(self.vocab					,							**self.added_tokens_encoder  )



						def __getstate__(			self	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.__dict__.copy()
								SCREAMING_SNAKE_CASE_					      =							None
								return state



						def __setstate__(			self	:   Optional[Any]					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							d

								# for backward compatibility
								if not hasattr(self					,							'sp_model_kwargs'  ):
										SCREAMING_SNAKE_CASE_					      =							{}

								SCREAMING_SNAKE_CASE_					      =							spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.sentencepiece_model_ckpt  )



						def 	__a					(			self	:   Dict					,							snake_case__	:   int  ):

								"""simple docstring"""

								return "".join((self.SP_CHAR_MAPPING.get(snake_case__					,							snake_case__  ) for c in text)  )



						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   Any					,							snake_case__	:   Optional[Any]=False					,							snake_case__	:   str=64					,							snake_case__	:   Tuple=0.1  ):

								"""simple docstring"""

								if self.sp_model_kwargs.get('enable_sampling'  ) is True:
										SCREAMING_SNAKE_CASE_					      =							True
								if self.sp_model_kwargs.get('alpha'  ) is not None:
										SCREAMING_SNAKE_CASE_					      =							self.sp_model_kwargs.get('alpha'  )
								if self.sp_model_kwargs.get('nbest_size'  ) is not None:
										SCREAMING_SNAKE_CASE_					      =							self.sp_model_kwargs.get('nbest_size'  )

								if not enable_sampling:
										SCREAMING_SNAKE_CASE_					      =							self.sp_model.EncodeAsPieces(snake_case__  )
								else:
										SCREAMING_SNAKE_CASE_					      =							self.sp_model.SampleEncodeAsPieces(snake_case__					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							[]
								for pi, piece in enumerate(snake_case__  ):
										if piece == SPIECE_UNDERLINE:
												if not pieces[pi + 1].startswith(snake_case__  ) and pi != 0:
														new_pieces.append(snake_case__  )
														continue
												else:
														continue
										SCREAMING_SNAKE_CASE_					      =							0
										for i, chunk in enumerate(snake_case__  ):
												if chunk == SPIECE_UNDERLINE:
														continue
												if self.is_ch_char(snake_case__  ) or self.is_punct(snake_case__  ):
														if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																new_pieces.append(piece[lst_i:i]  )
														new_pieces.append(snake_case__  )
														SCREAMING_SNAKE_CASE_					      =							i + 1
												elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
														if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																new_pieces.append(piece[lst_i:i]  )
														SCREAMING_SNAKE_CASE_					      =							i
												elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
														if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																new_pieces.append(piece[lst_i:i]  )
														SCREAMING_SNAKE_CASE_					      =							i
										if len(snake_case__  ) > lst_i:
												new_pieces.append(piece[lst_i:]  )
								return new_pieces



						def 	__a					(			self	:   int					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							''.join(snake_case__  ).replace(snake_case__					,							' '  ).strip()
								return out_string



						def 	__a					(			self	:   List[str]					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.convert_ids_to_tokens(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							''.join(snake_case__  ).replace(snake_case__					,							' '  ).strip()
								return out_string



						def 	__a					(			self	:   int					,							snake_case__	:   Union[str, Any]  ):

								"""simple docstring"""

								return self.vocab.get(snake_case__					,							self.vocab.get(self.unk_token  )  )



						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   int  ):

								"""simple docstring"""

								return self.reverse_vocab.get(snake_case__					,							self.unk_token  )



						def 	__a					(			self	:   Dict					,							snake_case__	:   Union[str, Any]					,							snake_case__	:   int=None  ):

								"""simple docstring"""

								if token_ids_a is None:
										return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								SCREAMING_SNAKE_CASE_					      =							[self.cls_token_id]
								SCREAMING_SNAKE_CASE_					      =							[self.sep_token_id]
								return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



						def 	__a					(			self	:   int					,							snake_case__	:   List[Any]					,							snake_case__	:   Union[str, Any]=None  ):

								"""simple docstring"""

								if offset_mapping_a is None:
										return [(0, 0)] + offset_mapping_a + [(0, 0)]

								return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



						def 	__a					(			self	:   Tuple					,							snake_case__	:   str					,							snake_case__	:   Dict=None					,							snake_case__	:   str=False  ):

								"""simple docstring"""

								if already_has_special_tokens:
										if token_ids_a is not None:
												raise ValueError(
												    'You should not supply a second sequence if the provided sequence of '
												    'ids is already formatted with special tokens for the model.'  )
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   List[int]					,							snake_case__	:   Optional[List[int]] = None  ):

								"""simple docstring"""

								if token_ids_a is None:
										# [CLS] X [SEP]
										return (len(snake_case__  ) + 2) * [0]

								# [CLS] A [SEP] [SEP] B [SEP]
								return [0] * (len(snake_case__  ) + 1) + [1] * (len(snake_case__  ) + 3)



						def 	__a					(			self	:   Optional[Any]					,							snake_case__	:   Any  ):

								"""simple docstring"""

								if "\u4e00" <= char <= "\u9fff":
										return True
								return False



						def 	__a					(			self	:   List[Any]					,							snake_case__	:   str  ):

								"""simple docstring"""

								if ("a" <= char <= "z") or ("A" <= char <= "Z"):
										return True
								return False



						def 	__a					(			self	:   Union[str, Any]					,							snake_case__	:   List[str]  ):

								"""simple docstring"""

								if char in ",;:.?!~，；：。？！《》【】":
										return True
								return False



						def 	__a					(			self	:   str					,							snake_case__	:   Tuple  ):

								"""simple docstring"""

								if char == " " or char == "\t" or char == "\n" or char == "\r":
										return True
								if len(snake_case__  ) == 1:
										SCREAMING_SNAKE_CASE_					      =							unicodedata.category(snake_case__  )
										if cat == "Zs":
												return True
								return False



						def 	__a					(			self	:   List[str]					,							snake_case__	:   Tuple  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{}
								with io.open(snake_case__					,							'r'					,							encoding='utf-8'  ) as f:
										for index, line in enumerate(snake_case__  ):
												SCREAMING_SNAKE_CASE_					      =							line.rstrip('\n'  )
												SCREAMING_SNAKE_CASE_					      =							int(snake_case__  )

								return token_to_idx






						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   str					,							snake_case__	:   Optional[str] = None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							0
								if os.path.isdir(snake_case__  ):
										SCREAMING_SNAKE_CASE_					      =							os.path.join(
										    snake_case__					,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
								else:
										SCREAMING_SNAKE_CASE_					      =							(filename_prefix + '-' if filename_prefix else '') + save_directory
								with open(snake_case__					,							'w'					,							encoding='utf-8'  ) as writer:
										for token, token_index in sorted(self.vocab.items()					,							key=lambda snake_case__  : kv[1]  ):
												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!'  )
														SCREAMING_SNAKE_CASE_					      =							token_index
												writer.write(token + '\n'  )
												index += 1

								SCREAMING_SNAKE_CASE_					      =							os.path.join(snake_case__					,							'sentencepiece.bpe.model'  )
								with open(snake_case__					,							'wb'  ) as fi:
										SCREAMING_SNAKE_CASE_					      =							self.sp_model.serialized_model_proto()
										fi.write(snake_case__  )

								return (vocab_file,)





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


SCREAMING_SNAKE_CASE:     int									= ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE:     Tuple									= {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
						raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Any									= ''' Hello world! cécé herlolip'''

SCREAMING_SNAKE_CASE:     Dict									= [
    ('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
    ('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
    ('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
    ('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]


def 					_a							(					lowerCAmelCase )->	Any:
		SCREAMING_SNAKE_CASE_					      =							[
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    '_float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase    ,						lowerCAmelCase )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )->	str:
		SCREAMING_SNAKE_CASE_					      =							dct.pop(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							val


def 					_a							(					lowerCAmelCase )->	List[str]:
		SCREAMING_SNAKE_CASE_					      =							torch.load(lowerCAmelCase    ,						map_location='cpu' )
		SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						'bart.large.cnn' ).eval()
		hub_interface.model.load_state_dict(sd['model'] )
		return hub_interface


def 					_a							(					lowerCAmelCase )->	Optional[Any]:
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							emb.weight.shape
		SCREAMING_SNAKE_CASE_					      =							nn.Linear(lowerCAmelCase    ,						lowerCAmelCase    ,						bias=lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							emb.weight.data
		return lin_layer


@torch.no_grad()
def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None )->	Tuple:
		if not os.path.exists(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							torch.hub.load('pytorch/fairseq'    ,						lowerCAmelCase ).eval()
		else:
				SCREAMING_SNAKE_CASE_					      =							load_xsum_checkpoint(lowerCAmelCase )

		bart.model.upgrade_state_dict(bart.model.state_dict() )
		if hf_checkpoint_name is None:
				SCREAMING_SNAKE_CASE_					      =							checkpoint_path.replace('.'    ,						'-' )
		SCREAMING_SNAKE_CASE_					      =							BartConfig.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							bart.encode(lowerCAmelCase ).unsqueeze(0 )
		SCREAMING_SNAKE_CASE_					      =							BartTokenizer.from_pretrained(lowerCAmelCase ).encode(lowerCAmelCase    ,						return_tensors='pt' ).unsqueeze(0 )
		if not torch.eq(lowerCAmelCase    ,						lowerCAmelCase ).all():
				raise ValueError(
				    F'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}''' )

		if checkpoint_path == "bart.large.mnli":
				SCREAMING_SNAKE_CASE_					      =							bart.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['model.decoder.embed_tokens.weight']
				for src, dest in mnli_rename_keys:
						rename_key(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							BartForSequenceClassification(lowerCAmelCase ).eval()
				model.load_state_dict(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							bart.predict('mnli'    ,						lowerCAmelCase    ,						return_logits=lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase )[0]  # logits
		else:  # no classification heads to worry about
				SCREAMING_SNAKE_CASE_					      =							bart.model.state_dict()
				remove_ignore_keys_(lowerCAmelCase )
				SCREAMING_SNAKE_CASE_					      =							state_dict['decoder.embed_tokens.weight']
				SCREAMING_SNAKE_CASE_					      =							bart.extract_features(lowerCAmelCase )
				if hf_checkpoint_name == "facebook/bart-large":
						SCREAMING_SNAKE_CASE_					      =							BartModel(lowerCAmelCase ).eval()
						model.load_state_dict(lowerCAmelCase )
						SCREAMING_SNAKE_CASE_					      =							model(lowerCAmelCase ).model[0]
				else:
						SCREAMING_SNAKE_CASE_					      =							BartForConditionalGeneration(lowerCAmelCase ).eval()  # an existing summarization ckpt
						model.model.load_state_dict(lowerCAmelCase )
						if hasattr(lowerCAmelCase    ,						'lm_head' ):
								SCREAMING_SNAKE_CASE_					      =							make_linear_from_emb(model.model.shared )
						SCREAMING_SNAKE_CASE_					      =							model.model(lowerCAmelCase )[0]

    # Check results
		if fairseq_output.shape != new_model_outputs.shape:
				raise ValueError(
				    F'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}''' )
		if (fairseq_output != new_model_outputs).any().item():
				raise ValueError('Some values in `fairseq_output` are different from `new_model_outputs`' )
		Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
		model.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:     Dict									= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
						)
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
						)
						SCREAMING_SNAKE_CASE:     List[str]									= parser.parse_args()
						convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)