



'''simple docstring'''


# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)






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

__UpperCAmelCase          =       ''' Hello world! cécé herlolip'''

__UpperCAmelCase          =       [
    ('''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	(   snake_case__       :    Union[str, Any]	)	->     List[str]:
   UpperCamelCase    :  int							=	[
       'encoder.version',
       'decoder.version',
       'model.encoder.version',
       'model.decoder.version',
       '_float_tensor',
   ]
   for k in ignore_keys:
      state_dict.pop(snake_case__       ,     snake_case__	)
def 		UpperCamelCase	(   snake_case__       :    int       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Any:
   UpperCamelCase    :  Dict							=	dct.pop(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	val
def 		UpperCamelCase	(   snake_case__       :    Dict	)	->     Tuple:
   UpperCamelCase    :  int							=	torch.load(snake_case__       ,     map_location='cpu'	)
   UpperCamelCase    :  Dict							=	torch.hub.load('pytorch/fairseq'       ,     'bart.large.cnn'	).eval()
   hub_interface.model.load_state_dict(sd['model']	)
   return hub_interface
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     Dict:
   UpperCamelCase	,     UpperCamelCase    :  str							=	emb.weight.shape
   UpperCamelCase    :  Optional[int]							=	nn.Linear(snake_case__       ,     snake_case__       ,     bias=snake_case__	)
   UpperCamelCase    :  List[str]							=	emb.weight.data
   return lin_layer
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    str=None	)	->     Optional[Any]:
   if not os.path.exists(snake_case__	):
      UpperCamelCase    :  List[str]							=	torch.hub.load('pytorch/fairseq'       ,     snake_case__	).eval()
   else:
      UpperCamelCase    :  int							=	load_xsum_checkpoint(snake_case__	)

   bart.model.upgrade_state_dict(bart.model.state_dict()	)
   if hf_checkpoint_name is None:
      UpperCamelCase    :  Tuple							=	checkpoint_path.replace('.'       ,     '-'	)
   UpperCamelCase    :  Optional[int]							=	BartConfig.from_pretrained(snake_case__	)
   UpperCamelCase    :  Optional[Any]							=	bart.encode(snake_case__	).unsqueeze(0	)
   UpperCamelCase    :  Any							=	BartTokenizer.from_pretrained(snake_case__	).encode(snake_case__       ,     return_tensors='pt'	).unsqueeze(0	)
   if not torch.eq(snake_case__       ,     snake_case__	).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_(snake_case__	)
      UpperCamelCase    :  int							=	state_dict['model.decoder.embed_tokens.weight']
      for src, dest in mnli_rename_keys:
         rename_key(snake_case__       ,     snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Any							=	BartForSequenceClassification(snake_case__	).eval()
      model.load_state_dict(snake_case__	)
      UpperCamelCase    :  Any							=	bart.predict('mnli'       ,     snake_case__       ,     return_logits=snake_case__	)
      UpperCamelCase    :  Tuple							=	model(snake_case__	)[0]  # logits
   else:  # no classification heads to worry about
      UpperCamelCase    :  List[str]							=	bart.model.state_dict()
      remove_ignore_keys_(snake_case__	)
      UpperCamelCase    :  List[str]							=	state_dict['decoder.embed_tokens.weight']
      UpperCamelCase    :  Union[str, Any]							=	bart.extract_features(snake_case__	)
      if hf_checkpoint_name == "facebook/bart-large":
         UpperCamelCase    :  List[str]							=	BartModel(snake_case__	).eval()
         model.load_state_dict(snake_case__	)
         UpperCamelCase    :  Optional[int]							=	model(snake_case__	).model[0]
      else:
         UpperCamelCase    :  Union[str, Any]							=	BartForConditionalGeneration(snake_case__	).eval()  # an existing summarization ckpt
         model.model.load_state_dict(snake_case__	)
         if hasattr(snake_case__       ,     'lm_head'	):
            UpperCamelCase    :  Optional[int]							=	make_linear_from_emb(model.model.shared	)
         UpperCamelCase    :  Dict							=	model.model(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(snake_case__	).mkdir(exist_ok=snake_case__	)
   model.save_pretrained(snake_case__	)


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



