






'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A	:			List[Any]         =logging.get_logger(__name__)

_A	:			int         ={'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}

class _lowercase	(				lowercase_		):
							a				      =       """ctrl"""
							a				      =       ["""past_key_values"""]
							a				      =       {
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}




							def __init__(							self:				Tuple			,							UpperCamelCase__:				Optional[Any]=246_534			,							UpperCamelCase__:				Union[str, Any]=256			,							UpperCamelCase__:				Optional[int]=1_280			,							UpperCamelCase__:				Any=8_192			,							UpperCamelCase__:				List[str]=48			,							UpperCamelCase__:				List[Any]=16			,							UpperCamelCase__:				Any=0.1			,							UpperCamelCase__:				List[str]=0.1			,							UpperCamelCase__:				str=1e-6			,							UpperCamelCase__:				Optional[int]=0.02			,							UpperCamelCase__:				Dict=True			,							**UpperCamelCase__:				Dict			,							):
														lowerCamelCase__					:	Optional[Any]         =       vocab_size
														lowerCamelCase__					:	List[Any]         =       n_positions
														lowerCamelCase__					:	Optional[Any]         =       n_embd
														lowerCamelCase__					:	Any         =       n_layer
														lowerCamelCase__					:	List[str]         =       n_head
														lowerCamelCase__					:	str         =       dff
														lowerCamelCase__					:	List[str]         =       resid_pdrop
														lowerCamelCase__					:	Dict         =       embd_pdrop
														lowerCamelCase__					:	Optional[int]         =       layer_norm_epsilon
														lowerCamelCase__					:	List[str]         =       initializer_range

														lowerCamelCase__					:	List[Any]         =       use_cache

														super().__init__(**UpperCamelCase__     )





'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   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='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




