

"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		__lowerCAmelCase:       List[Any]          =			[
		    '''encoder.version''',
		    '''decoder.version''',
		    '''model.encoder.version''',
		    '''model.decoder.version''',
		    '''_float_tensor''',
		    '''decoder.output_projection.weight''',
		]
		for k in ignore_keys:
				state_dict.pop(__lowerCamelCase					, __lowerCamelCase )






def     a__    (    __SCREAMING_SNAKE_CASE )	->	List[str]:
		__lowerCAmelCase:       Dict          =			emb.weight.shape
		__lowerCAmelCase:       List[Any]          =			nn.Linear(__lowerCamelCase					, __lowerCamelCase					, bias=__lowerCamelCase )
		__lowerCAmelCase:       Optional[int]          =			emb.weight.data
		return lin_layer






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE="facebook/mbart-large-en-ro"					, __SCREAMING_SNAKE_CASE=False					, __SCREAMING_SNAKE_CASE=False )	->	Optional[int]:
		__lowerCAmelCase:       int          =			torch.load(__lowerCamelCase					, map_location="cpu" )['''model''']
		remove_ignore_keys_(__lowerCamelCase )
		__lowerCAmelCase:       Dict          =			state_dict['''encoder.embed_tokens.weight'''].shape[0]

		__lowerCAmelCase:       Optional[int]          =			MBartConfig.from_pretrained(__lowerCamelCase					, vocab_size=__lowerCamelCase )
		if mbart_aa and finetuned:
				__lowerCAmelCase:       Tuple          =			'''relu'''

		__lowerCAmelCase:       List[Any]          =			state_dict['''decoder.embed_tokens.weight''']
		__lowerCAmelCase:       Tuple          =			MBartForConditionalGeneration(__lowerCamelCase )
		model.model.load_state_dict(__lowerCamelCase )

		if finetuned:
				__lowerCAmelCase:       Any          =			make_linear_from_emb(model.model.shared )

		return model


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="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")
			__A						   =			parser.parse_args()
			__A						   =			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)







"""simple docstring"""







from ...configuration_utils import PretrainedConfig


lowerCAmelCase_   						=   {
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}
class        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					str   	=				"tapas"







		def __init__(	self						:  List[Any]			,_snake_case						:  Dict=30_522			,_snake_case						:  Union[str, Any]=768			,_snake_case						:  int=12			,_snake_case						:  Union[str, Any]=12			,_snake_case						:  Union[str, Any]=3_072			,_snake_case						:  List[Any]="gelu"			,_snake_case						:  Optional[int]=0.1			,_snake_case						:  Tuple=0.1			,_snake_case						:  List[Any]=1_024			,_snake_case						:  Any=[3, 256, 256, 2, 256, 256, 10]			,_snake_case						:  List[Any]=0.02			,_snake_case						:  Union[str, Any]=1e-12			,_snake_case						:  str=0			,_snake_case						:  Any=10.0			,_snake_case						:  int=0			,_snake_case						:  Optional[Any]=1.0			,_snake_case						:  List[str]=None			,_snake_case						:  Tuple=1.0			,_snake_case						:  Tuple=False			,_snake_case						:  List[Any]=None			,_snake_case						:  int=1.0			,_snake_case						:  List[Any]=1.0			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]=False			,_snake_case						:  Optional[int]="ratio"			,_snake_case						:  Any=None			,_snake_case						:  Union[str, Any]=None			,_snake_case						:  List[str]=64			,_snake_case						:  Optional[Any]=32			,_snake_case						:  Optional[Any]=False			,_snake_case						:  Optional[int]=True			,_snake_case						:  Dict=False			,_snake_case						:  Tuple=False			,_snake_case						:  int=True			,_snake_case						:  List[str]=False			,_snake_case						:  Dict=None			,_snake_case						:  Optional[int]=None			,**_snake_case						:  int			,)					->		List[Any]:






				"""simple docstring"""



				super().__init__(pad_token_id=_snake_case			,**_snake_case						)

				# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
				lowercase__   :    Optional[int]				=			vocab_size
				lowercase__   :    List[str]				=			hidden_size
				lowercase__   :    Any				=			num_hidden_layers
				lowercase__   :    Optional[Any]				=			num_attention_heads
				lowercase__   :    Optional[int]				=			hidden_act
				lowercase__   :    List[Any]				=			intermediate_size
				lowercase__   :    List[Any]				=			hidden_dropout_prob
				lowercase__   :    Dict				=			attention_probs_dropout_prob
				lowercase__   :    str				=			max_position_embeddings
				lowercase__   :    Dict				=			type_vocab_sizes
				lowercase__   :    Optional[Any]				=			initializer_range
				lowercase__   :    Dict				=			layer_norm_eps

				# Fine-tuning task hyperparameters
				lowercase__   :    Any				=			positive_label_weight
				lowercase__   :    int				=			num_aggregation_labels
				lowercase__   :    List[str]				=			aggregation_loss_weight
				lowercase__   :    Optional[int]				=			use_answer_as_supervision
				lowercase__   :    Optional[Any]				=			answer_loss_importance
				lowercase__   :    Union[str, Any]				=			use_normalized_answer_loss
				lowercase__   :    str				=			huber_loss_delta
				lowercase__   :    str				=			temperature
				lowercase__   :    int				=			aggregation_temperature
				lowercase__   :    List[Any]				=			use_gumbel_for_cells
				lowercase__   :    Tuple				=			use_gumbel_for_aggregation
				lowercase__   :    Union[str, Any]				=			average_approximation_function
				lowercase__   :    Union[str, Any]				=			cell_selection_preference
				lowercase__   :    Any				=			answer_loss_cutoff
				lowercase__   :    List[Any]				=			max_num_rows
				lowercase__   :    str				=			max_num_columns
				lowercase__   :    int				=			average_logits_per_cell
				lowercase__   :    str				=			select_one_column
				lowercase__   :    str				=			allow_empty_column_selection
				lowercase__   :    Any				=			init_cell_selection_weights_to_zero
				lowercase__   :    Optional[int]				=			reset_position_index_per_cell
				lowercase__   :    Union[str, Any]				=			disable_per_token_loss

				# Aggregation hyperparameters
				lowercase__   :    Optional[Any]				=			aggregation_labels
				lowercase__   :    List[Any]				=			no_aggregation_label_index

				if isinstance(self.aggregation_labels			,_snake_case						):
						lowercase__   :    Union[str, Any]				=			{int(_snake_case						): v for k, v in aggregation_labels.items()}
