



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_barthez import BarthezTokenizer
else:
	__A       =						None

__A       =						logging.get_logger(__name__)

__A       =						{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A       =						{
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'''
        ),
    },
}

__A       =						{
    '''moussaKam/mbarthez''': 1024,
    '''moussaKam/barthez''': 1024,
    '''moussaKam/barthez-orangesum-title''': 1024,
}

__A       =						'''▁'''






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: Dict		       =							VOCAB_FILES_NAMES
	a__				: List[str]		       =							PRETRAINED_VOCAB_FILES_MAP
	a__				: List[str]		       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a__				: Dict		       =							["input_ids", "attention_mask"]
	a__				: List[Any]		       =							BarthezTokenizer




	def __init__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Optional[int]=None						,  __UpperCAmelCase     :						int=None						,  __UpperCAmelCase     :						List[Any]="<s>"						,  __UpperCAmelCase     :						Any="</s>"						,  __UpperCAmelCase     :						Optional[int]="</s>"						,  __UpperCAmelCase     :						Union[str, Any]="<s>"						,  __UpperCAmelCase     :						Optional[Any]="<unk>"						,  __UpperCAmelCase     :						Tuple="<pad>"						,  __UpperCAmelCase     :						Optional[Any]="<mask>"						,  **__UpperCAmelCase     :						Any						,  )      ->			Optional[int]:
							# Mask token behave like a normal word, i.e. include the space before it
							UpperCAmelCase_=       AddedToken(__UpperCAmelCase						,  lstrip=__UpperCAmelCase						,  rstrip=__UpperCAmelCase					) if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) else mask_token

							super().__init__(
							    __UpperCAmelCase						,  tokenizer_file=__UpperCAmelCase						,  bos_token=__UpperCAmelCase						,  eos_token=__UpperCAmelCase						,  unk_token=__UpperCAmelCase						,  sep_token=__UpperCAmelCase						,  cls_token=__UpperCAmelCase						,  pad_token=__UpperCAmelCase						,  mask_token=__UpperCAmelCase						,  **__UpperCAmelCase						,  )

							UpperCAmelCase_=       vocab_file
							UpperCAmelCase_=       False if not self.vocab_file else True




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:

							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCAmelCase_=       [self.cls_token_id]
							UpperCAmelCase_=       [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[int]						,  __UpperCAmelCase     :						Optional[List[int]] = None					)      ->			List[int]:
							UpperCAmelCase_=       [self.sep_token_id]
							UpperCAmelCase_=       [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     :						Union[str, Any]						,  __UpperCAmelCase     :						str						,  __UpperCAmelCase     :						Optional[str] = None					)      ->			Tuple[str]:
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
													    """tokenizer."""					)

							if not os.path.isdir(__UpperCAmelCase					):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
							UpperCAmelCase_=       os.path.join(
							    __UpperCAmelCase						,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(__UpperCAmelCase					):
													copyfile(self.vocab_file						,  __UpperCAmelCase					)

							return (out_vocab_file,)









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       Namespace(**checkpoint["""cfg"""]["""model"""]    )
						UpperCAmelCase_=       checkpoint["""model"""]
						remove_ignore_keys_(lowerCAmelCase_    )
						UpperCAmelCase_=       state_dict["""decoder.embed_tokens.weight"""].shape[0]

						UpperCAmelCase_=       {key.replace("""decoder"""      ,"""model"""    ): val for key, val in state_dict.items()}

						UpperCAmelCase_=       XGLMConfig(
						    vocab_size=lowerCAmelCase_      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="""gelu"""      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

						UpperCAmelCase_=       XGLMForCausalLM(lowerCAmelCase_    )
						UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						print(lowerCAmelCase_    )
						UpperCAmelCase_=       make_linear_from_emb(model.model.embed_tokens    )

						return model


if __name__ == "__main__":
	__A       =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('''fairseq_path''', type=str, help='''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.''')
	__A       =						parser.parse_args()
	__A       =						convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)




