

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


lowerCAmelCase_			      =      ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
lowerCAmelCase_			      =      {'''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()
lowerCAmelCase_			      =      logging.get_logger(__name__)

lowerCAmelCase_			      =      ''' Hello world! cécé herlolip'''

lowerCAmelCase_			      =      [
    ('''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     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      [
						    '''encoder.version''',
						    '''decoder.version''',
						    '''model.encoder.version''',
						    '''model.decoder.version''',
						    '''_float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      dct.pop(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      val


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      torch.load(SCREAMING_SNAKE_CASE__    ,						map_location='''cpu'''			)
						snake_case_  =      torch.hub.load('''pytorch/fairseq'''    ,						'''bart.large.cnn'''			).eval()
						hub_interface.model.load_state_dict(sd['''model''']			)
						return hub_interface


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_,       snake_case_  =      emb.weight.shape
						snake_case_  =      nn.Linear(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						bias=SCREAMING_SNAKE_CASE__			)
						snake_case_  =      emb.weight.data
						return lin_layer


@torch.no_grad()
def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None			):
						if not os.path.exists(SCREAMING_SNAKE_CASE__			):
												snake_case_  =      torch.hub.load('''pytorch/fairseq'''    ,						SCREAMING_SNAKE_CASE__			).eval()
						else:
												snake_case_  =      load_xsum_checkpoint(SCREAMING_SNAKE_CASE__			)

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

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


if __name__ == "__main__":
					lowerCAmelCase_			      =      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'''
					)
					lowerCAmelCase_			      =      parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
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_albert import AlbertTokenizer
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		None

SCREAMING_SNAKE_CASE			:						str				  =		logging.get_logger(__name__)
SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE			:						Any				  =		{
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		"""▁"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      AlbertTokenizer

  def __init__(		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				__SCREAMING_SNAKE_CASE			:   Dict=True  ,				__SCREAMING_SNAKE_CASE			:   Tuple=True  ,				__SCREAMING_SNAKE_CASE			:   Dict=False  ,				__SCREAMING_SNAKE_CASE			:   Dict="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Dict="<unk>"  ,				__SCREAMING_SNAKE_CASE			:   str="[SEP]"  ,				__SCREAMING_SNAKE_CASE			:   Any="<pad>"  ,				__SCREAMING_SNAKE_CASE			:   List[str]="[CLS]"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]="[MASK]"  ,				**__SCREAMING_SNAKE_CASE			:   Tuple  ,				):
   # Mask token behave like a normal word, i.e. include the space before it and
   # is included in the raw text, there should be a match in a non-normalized sentence.
   __a							=	(
       AddedToken(__SCREAMING_SNAKE_CASE  ,				lstrip=__SCREAMING_SNAKE_CASE  ,				rstrip=__SCREAMING_SNAKE_CASE  ,				normalized=__SCREAMING_SNAKE_CASE							)
       if isinstance(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
       else mask_token
   )

   super().__init__(
       __SCREAMING_SNAKE_CASE  ,				tokenizer_file=__SCREAMING_SNAKE_CASE  ,				do_lower_case=__SCREAMING_SNAKE_CASE  ,				remove_space=__SCREAMING_SNAKE_CASE  ,				keep_accents=__SCREAMING_SNAKE_CASE  ,				bos_token=__SCREAMING_SNAKE_CASE  ,				eos_token=__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  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	do_lower_case
   __a							=	remove_space
   __a							=	keep_accents
   __a							=	vocab_file
   __a							=	False if not self.vocab_file else True

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[self.cls_token_id]
   if token_ids_a is None:
    return cls + token_ids_a + sep
   return cls + token_ids_a + sep + token_ids_a + sep

  def 		_UpperCAmelCase (		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[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 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None							):
   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(__SCREAMING_SNAKE_CASE							):
    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
    return
   __a							=	os.path.join(
       __SCREAMING_SNAKE_CASE  ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

   return (out_vocab_file,)


