'''simple docstring'''
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


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

a=  ''' Hello world! cécé herlolip'''

a=  [
    ('''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  _UpperCamelCase (     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  [
        'encoder.version',
        'decoder.version',
        'model.encoder.version',
        'model.decoder.version',
        '_float_tensor',
    ]
    for k in ignore_keys:
        state_dict.pop(_a				,     _a			)







def  _UpperCamelCase (     _a	:	Optional[Any]				,     _a	:	Optional[int]				,     _a	:	List[Any]			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  dct.pop(_a			)
    __UpperCamelCase	:	int		    =  val







def  _UpperCamelCase (     _a	:	int			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  torch.load(_a				,     map_location='cpu'			)
    __UpperCamelCase	:	str		    =  torch.hub.load('pytorch/fairseq'				,     'bart.large.cnn'			).eval()
    hub_interface.model.load_state_dict(sd['model']			)
    return hub_interface







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	Optional[int]		    =  emb.weight.shape
    __UpperCamelCase	:	Union[str, Any]		    =  nn.Linear(_a				,     _a				,     bias=_a			)
    __UpperCamelCase	:	Optional[int]		    =  emb.weight.data
    return lin_layer







@torch.no_grad()
def  _UpperCamelCase (     _a	:	Tuple				,     _a	:	Any				,     _a	:	Union[str, Any]=None			):




    """simple docstring"""



    if not os.path.exists(_a			):
        __UpperCamelCase	:	Union[str, Any]		    =  torch.hub.load('pytorch/fairseq'				,     _a			).eval()
    else:
        __UpperCamelCase	:	Optional[Any]		    =  load_xsum_checkpoint(_a			)

    bart.model.upgrade_state_dict(bart.model.state_dict()			)
    if hf_checkpoint_name is None:
        __UpperCamelCase	:	Optional[int]		    =  checkpoint_path.replace('.'				,     '-'			)
    __UpperCamelCase	:	Any		    =  BartConfig.from_pretrained(_a			)
    __UpperCamelCase	:	Dict		    =  bart.encode(_a			).unsqueeze(0			)
    __UpperCamelCase	:	str		    =  BartTokenizer.from_pretrained(_a			).encode(_a				,     return_tensors='pt'			).unsqueeze(0			)
    if not torch.eq(_a				,     _a			).all():
        raise ValueError(
            f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""			)

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


if __name__ == "__main__":
  a=  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'''
  )
  a=  parser.parse_args()
  convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)




'''simple docstring'''
import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizer
     SCREAMING_SNAKE_CASE__		   =						LayoutLMTokenizerFast
     SCREAMING_SNAKE_CASE__		   =						True
     SCREAMING_SNAKE_CASE__		   =						True







     def lowerCAmelCase     (		self    ):
         super().setUp()

         __UpperCamelCase	:	List[str]		    =  [
             '[UNK]',
             '[CLS]',
             '[SEP]',
             'want',
             '##want',
             '##ed',
             'wa',
             'un',
             'runn',
             '##ing',
             ',',
             'low',
             'lowest',
         ]
         __UpperCamelCase	:	Union[str, Any]		    =  os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']    )
         with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'    ) as vocab_writer:
             vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  'UNwant\u00E9d,running'
         __UpperCamelCase	:	Union[str, Any]		    =  'unwanted, running'
         return input_text, output_text







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.tokenizer_class(self.vocab_file    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.tokenize('UNwant\u00E9d,running'    )
         self.assertListEqual(_lowerCamelCase     ,			['un', '##want', '##ed', ',', 'runn', '##ing']    )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )     ,			[7, 4, 5, 1_0, 8, 9]    )







     def lowerCAmelCase     (		self    ):
         pass



