


"""simple docstring"""
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				str:
							snake_case_           =		[
							    """encoder.version""",
							    """decoder.version""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """_float_tensor""",
							    """decoder.output_projection.weight""",
							]
							for k in ignore_keys:
														state_dict.pop(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							snake_case_       ,						snake_case_           =		emb.weight.shape
							snake_case_           =		nn.Linear(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, bias=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		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				)    ->				Tuple:
							snake_case_           =		torch.load(_SCREAMING_SNAKE_CASE				, map_location="""cpu"""				)["""model"""]
							remove_ignore_keys_(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		state_dict["""encoder.embed_tokens.weight"""].shape[0]

							snake_case_           =		MBartConfig.from_pretrained(_SCREAMING_SNAKE_CASE				, vocab_size=_SCREAMING_SNAKE_CASE				)
							if mbart_aa and finetuned:
														snake_case_           =		"""relu"""

							snake_case_           =		state_dict["""decoder.embed_tokens.weight"""]
							snake_case_           =		MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
							model.model.load_state_dict(_SCREAMING_SNAKE_CASE				)

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

							return model


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    List[str]		=    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')
				__SCREAMING_SNAKE_CASE							:    Dict		=    parser.parse_args()
				__SCREAMING_SNAKE_CASE							:    Any		=    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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    [
				    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BioGptForCausalLM',
				    'BioGptForTokenClassification',
				    'BioGptForSequenceClassification',
				    'BioGptModel',
				    'BioGptPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
				from .tokenization_biogpt import BioGptTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_biogpt import (
								    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BioGptForCausalLM,
								    BioGptForSequenceClassification,
								    BioGptForTokenClassification,
								    BioGptModel,
								    BioGptPreTrainedModel,
								)


else:
				import sys

				__SCREAMING_SNAKE_CASE							:    Dict		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



