




'''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


UpperCamelCase_						:      List[Any]       =						['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
UpperCamelCase_						:      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()
UpperCamelCase_						:      List[Any]       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[Any]       =						''' Hello world! cécé herlolip'''

UpperCamelCase_						:      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     (       _UpperCamelCase:   Union[str, Any]   )						->			Optional[Any]:

					"""simple docstring"""




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





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Any ,						_UpperCamelCase:   Optional[int]   )						->			Optional[int]:

					"""simple docstring"""




					_snake_case							=				dct.pop(_UpperCamelCase   )
					_snake_case							=				val





def      __a     (       _UpperCamelCase:   Dict   )						->			int:

					"""simple docstring"""




					_snake_case							=				torch.load(_UpperCamelCase ,						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      __a     (       _UpperCamelCase:   Optional[int]   )						->			Tuple:

					"""simple docstring"""




					_snake_case							, _snake_case							=				emb.weight.shape
					_snake_case							=				nn.Linear(_UpperCamelCase ,						_UpperCamelCase ,						bias=_UpperCamelCase   )
					_snake_case							=				emb.weight.data
					return lin_layer





@torch.no_grad()
def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   List[Any]=None   )						->			int:

					"""simple docstring"""




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

					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(_UpperCamelCase   )
					_snake_case							=				bart.encode(_UpperCamelCase   ).unsqueeze(0   )
					_snake_case							=				BartTokenizer.from_pretrained(_UpperCamelCase   ).encode(_UpperCamelCase ,						return_tensors="pt"   ).unsqueeze(0   )
					if not torch.eq(_UpperCamelCase ,						_UpperCamelCase   ).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_(_UpperCamelCase   )
										_snake_case							=				state_dict["model.decoder.embed_tokens.weight"]
										for src, dest in mnli_rename_keys:
															rename_key(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
										_snake_case							=				BartForSequenceClassification(_UpperCamelCase   ).eval()
										model.load_state_dict(_UpperCamelCase   )
										_snake_case							=				bart.predict("mnli" ,						_UpperCamelCase ,						return_logits=_UpperCamelCase   )
										_snake_case							=				model(_UpperCamelCase   )[0]  # logits
					else:  # no classification heads to worry about
										_snake_case							=				bart.model.state_dict()
										remove_ignore_keys_(_UpperCamelCase   )
										_snake_case							=				state_dict["decoder.embed_tokens.weight"]
										_snake_case							=				bart.extract_features(_UpperCamelCase   )
										if hf_checkpoint_name == "facebook/bart-large":
															_snake_case							=				BartModel(_UpperCamelCase   ).eval()
															model.load_state_dict(_UpperCamelCase   )
															_snake_case							=				model(_UpperCamelCase   ).model[0]
										else:
															_snake_case							=				BartForConditionalGeneration(_UpperCamelCase   ).eval()  # an existing summarization ckpt
															model.model.load_state_dict(_UpperCamelCase   )
															if hasattr(_UpperCamelCase ,						"lm_head"   ):
																				_snake_case							=				make_linear_from_emb(model.model.shared   )
															_snake_case							=				model.model(_UpperCamelCase   )[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(_UpperCamelCase   ).mkdir(exist_ok=_UpperCamelCase   )
					model.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
			UpperCamelCase_						:      List[Any]       =						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'''
			)
			UpperCamelCase_						:      List[str]       =						parser.parse_args()
			convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)








'''simple docstring'''





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class        _a    :
     SCREAMING_SNAKE_CASE_							:   int
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None
     SCREAMING_SNAKE_CASE_							:   TreeNode | None       =						None







UpperCamelCase_						:      Any       =						namedtuple('''CoinsDistribResult''', '''moves excess''')





def      __a     (       _UpperCamelCase:   TreeNode | None   )						->			int:

     """simple docstring"""




     if root is None:
          return 0

     # Validation
     def count_nodes(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_nodes(node.left   ) + count_nodes(node.right   ) + 1

     def count_coins(_UpperCamelCase:   TreeNode | None   ) -> int:
          if node is None:
               return 0

          return count_coins(node.left   ) + count_coins(node.right   ) + node.data

     if count_nodes(_UpperCamelCase   ) != count_coins(_UpperCamelCase   ):
          raise ValueError("The nodes number should be same as the number of coins"   )

     # Main calculation
     def get_distrib(_UpperCamelCase:   TreeNode | None   ) -> CoinsDistribResult:

          if node is None:
               return CoinsDistribResult(0 ,						1   )

          _snake_case							, _snake_case							=				get_distrib(node.left   )
          _snake_case							, _snake_case							=				get_distrib(node.right   )

          _snake_case							=				1 - left_distrib_excess
          _snake_case							=				1 - right_distrib_excess

          _snake_case							=				(
              left_distrib_moves
              + right_distrib_moves
              + abs(_UpperCamelCase   )
              + abs(_UpperCamelCase   )
          )
          _snake_case							=				node.data - coins_to_left - coins_to_right

          return CoinsDistribResult(_UpperCamelCase ,						_UpperCamelCase   )

     return get_distrib(_UpperCamelCase   )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()


