import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 			A__      (	snake_case_ :		List[str]      ):
 SCREAMING_SNAKE_CASE__:   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(snake_case_       ,				snake_case_      )







def 			A__      (	snake_case_ :		Any      ):
 SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Dict=      emb.weight.shape
 SCREAMING_SNAKE_CASE__:   List[Any]=      nn.Linear(snake_case_       ,				snake_case_       ,				bias=snake_case_      )
 SCREAMING_SNAKE_CASE__:   Any=      emb.weight.data
 return lin_layer







def 			A__      (	snake_case_ :		Optional[Any]       ,				snake_case_ :		Any="facebook/mbart-large-en-ro"       ,				snake_case_ :		Optional[int]=False       ,				snake_case_ :		Optional[Any]=False      ):
 SCREAMING_SNAKE_CASE__:   List[str]=      torch.load(snake_case_       ,				map_location='''cpu'''      )['''model''']
 remove_ignore_keys_(snake_case_      )
 SCREAMING_SNAKE_CASE__:   Dict=      state_dict['''encoder.embed_tokens.weight'''].shape[0]

 SCREAMING_SNAKE_CASE__:   List[Any]=      MBartConfig.from_pretrained(snake_case_       ,				vocab_size=snake_case_      )
 if mbart_aa and finetuned:
  SCREAMING_SNAKE_CASE__:   List[Any]=      '''relu'''

 SCREAMING_SNAKE_CASE__:   List[str]=      state_dict['''decoder.embed_tokens.weight''']
 SCREAMING_SNAKE_CASE__:   Tuple=      MBartForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_      )

 if finetuned:
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
      lowercase_     :   List[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')
      lowercase_     :   Dict 				=				parser.parse_args()
      lowercase_     :   Union[str, 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)


from typing import TYPE_CHECKING

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


lowercase_     :   List[str] 				=				{
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   Optional[Any] 				=				['MaskFormerFeatureExtractor']
      lowercase_     :   Dict 				=				['MaskFormerImageProcessor']


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_     :   str 				=				[
          'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MaskFormerForInstanceSegmentation',
          'MaskFormerModel',
          'MaskFormerPreTrainedModel',
      ]
      lowercase_     :   Optional[Any] 				=				[
          'MaskFormerSwinBackbone',
          'MaskFormerSwinModel',
          'MaskFormerSwinPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
      from .configuration_maskformer_swin import MaskFormerSwinConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_maskformer import MaskFormerFeatureExtractor
            from .image_processing_maskformer import MaskFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_maskformer import (
                MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaskFormerForInstanceSegmentation,
                MaskFormerModel,
                MaskFormerPreTrainedModel,
            )
            from .modeling_maskformer_swin import (
                MaskFormerSwinBackbone,
                MaskFormerSwinModel,
                MaskFormerSwinPreTrainedModel,
            )


else:
      import sys

      lowercase_     :   Any 				=				_LazyModule(__name__, globals()['__file__'], _import_structure)

