


"""simple docstring"""




import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def       a_ (	lowercase__ :List[str]     ):
			__lowerCamelCase          =      [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """_float_tensor""",
			    """decoder.output_projection.weight""",
			]
			for k in ignore_keys:
						state_dict.pop(lowercase__,			lowercase__     )





def       a_ (	lowercase__ :List[Any]     ):
			__lowerCamelCase     ,__lowerCamelCase          =      emb.weight.shape
			__lowerCamelCase          =      nn.Linear(lowercase__,			lowercase__,			bias=lowercase__     )
			__lowerCamelCase          =      emb.weight.data
			return lin_layer





def       a_ (	lowercase__ :Dict,			lowercase__ :Tuple="facebook/mbart-large-en-ro",			lowercase__ :List[Any]=False,			lowercase__ :Optional[Any]=False     ):
			__lowerCamelCase          =      torch.load(lowercase__,			map_location="""cpu"""     )["""model"""]
			remove_ignore_keys_(lowercase__     )
			__lowerCamelCase          =      state_dict["""encoder.embed_tokens.weight"""].shape[0]

			__lowerCamelCase          =      MBartConfig.from_pretrained(lowercase__,			vocab_size=lowercase__     )
			if mbart_aa and finetuned:
						__lowerCamelCase          =      """relu"""

			__lowerCamelCase          =      state_dict["""decoder.embed_tokens.weight"""]
			__lowerCamelCase          =      MBartForConditionalGeneration(lowercase__     )
			model.model.load_state_dict(lowercase__     )

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

			return model


if __name__ == "__main__":
					__magic_name__						: 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')
					__magic_name__						: Union[str, Any]    =				parser.parse_args()
					__magic_name__						: Optional[int]    =				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_torch_available, is_vision_available


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




