






"""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


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

lowercase    			=       ''' Hello world! cécé herlolip'''

lowercase    			=       [
    ('''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 _lowerCAmelCase	(  __lowerCamelCase:Tuple   ):

						'''simple docstring'''






						__magic_name__            =     [
						    "encoder.version",
						    "decoder.version",
						    "model.encoder.version",
						    "model.decoder.version",
						    "_float_tensor",
						]
						for k in ignore_keys:
												state_dict.pop(__lowerCamelCase ,						__lowerCamelCase   )






def _lowerCAmelCase	(  __lowerCamelCase:Union[str, Any] ,						__lowerCamelCase:Tuple ,						__lowerCamelCase:Any   ):

						'''simple docstring'''






						__magic_name__            =     dct.pop(__lowerCamelCase   )
						__magic_name__            =     val






def _lowerCAmelCase	(  __lowerCamelCase:List[Any]   ):

						'''simple docstring'''






						__magic_name__            =     torch.load(__lowerCamelCase ,						map_location="cpu"   )
						__magic_name__            =     torch.hub.load("pytorch/fairseq" ,						"bart.large.cnn"   ).eval()
						hub_interface.model.load_state_dict(sd["model"]   )
						return hub_interface






def _lowerCAmelCase	(  __lowerCamelCase:List[str]   ):

						'''simple docstring'''






						__magic_name__      ,    __magic_name__            =     emb.weight.shape
						__magic_name__            =     nn.Linear(__lowerCamelCase ,						__lowerCamelCase ,						bias=__lowerCamelCase   )
						__magic_name__            =     emb.weight.data
						return lin_layer






@torch.no_grad()
def _lowerCAmelCase	(  __lowerCamelCase:Optional[Any] ,						__lowerCamelCase:Any ,						__lowerCamelCase:Optional[int]=None   ):

						'''simple docstring'''






						if not os.path.exists(__lowerCamelCase   ):
												__magic_name__            =     torch.hub.load("pytorch/fairseq" ,						__lowerCamelCase   ).eval()
						else:
												__magic_name__            =     load_xsum_checkpoint(__lowerCamelCase   )

						bart.model.upgrade_state_dict(bart.model.state_dict()   )
						if hf_checkpoint_name is None:
												__magic_name__            =     checkpoint_path.replace("." ,						"-"   )
						__magic_name__            =     BartConfig.from_pretrained(__lowerCamelCase   )
						__magic_name__            =     bart.encode(__lowerCamelCase   ).unsqueeze(0   )
						__magic_name__            =     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":
												__magic_name__            =     bart.state_dict()
												remove_ignore_keys_(__lowerCamelCase   )
												__magic_name__            =     state_dict["model.decoder.embed_tokens.weight"]
												for src, dest in mnli_rename_keys:
																		rename_key(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )
												__magic_name__            =     BartForSequenceClassification(__lowerCamelCase   ).eval()
												model.load_state_dict(__lowerCamelCase   )
												__magic_name__            =     bart.predict("mnli" ,						__lowerCamelCase ,						return_logits=__lowerCamelCase   )
												__magic_name__            =     model(__lowerCamelCase   )[0]  # logits
						else:  # no classification heads to worry about
												__magic_name__            =     bart.model.state_dict()
												remove_ignore_keys_(__lowerCamelCase   )
												__magic_name__            =     state_dict["decoder.embed_tokens.weight"]
												__magic_name__            =     bart.extract_features(__lowerCamelCase   )
												if hf_checkpoint_name == "facebook/bart-large":
																		__magic_name__            =     BartModel(__lowerCamelCase   ).eval()
																		model.load_state_dict(__lowerCamelCase   )
																		__magic_name__            =     model(__lowerCamelCase   ).model[0]
												else:
																		__magic_name__            =     BartForConditionalGeneration(__lowerCamelCase   ).eval()  # an existing summarization ckpt
																		model.model.load_state_dict(__lowerCamelCase   )
																		if hasattr(__lowerCamelCase ,						"lm_head"   ):
																								__magic_name__            =     make_linear_from_emb(model.model.shared   )
																		__magic_name__            =     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__":
			lowercase    			=       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'''
			)
			lowercase    			=       parser.parse_args()
			convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:str   ):

						'''simple docstring'''






						return "".join(chr(ord(__lowerCamelCase   ) - 3_2   ) if "a" <= char <= "z" else char for char in word   )


if __name__ == "__main__":
			from doctest import testmod

			testmod()



