


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def   _UpperCAmelCase       (     UpperCAmelCase      :			int      ):
      """simple docstring"""




      __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(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase						,  __lowerCamelCase     :      Optional[int]       						=						emb.weight.shape
      __lowerCamelCase     :      str       						=						nn.Linear(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	bias=_SCREAMING_SNAKE_CASE      )
      __lowerCamelCase     :      Tuple       						=						emb.weight.data
      return lin_layer





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			int="facebook/mbart-large-en-ro"						,	UpperCAmelCase      :			Optional[Any]=False						,	UpperCAmelCase      :			Any=False      ):
      """simple docstring"""




      __lowerCamelCase     :      Any       						=						torch.load(_SCREAMING_SNAKE_CASE						,	map_location="""cpu"""      )["""model"""]
      remove_ignore_keys_(_SCREAMING_SNAKE_CASE      )
      __lowerCamelCase     :      Optional[Any]       						=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

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

      __lowerCamelCase     :      List[str]       						=						state_dict["""decoder.embed_tokens.weight"""]
      __lowerCamelCase     :      List[Any]       						=						MBartForConditionalGeneration(_SCREAMING_SNAKE_CASE      )
      model.model.load_state_dict(_SCREAMING_SNAKE_CASE      )

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

      return model


if __name__ == "__main__":
    __UpperCamelCase    :						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')
    __UpperCamelCase    :						Dict     =							parser.parse_args()
    __UpperCamelCase    :						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


__UpperCamelCase    :						Any     =							{
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __UpperCamelCase    :						int     =							[
        'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TimesformerModel',
        'TimesformerForVideoClassification',
        'TimesformerPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_timesformer import (
            TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            TimesformerForVideoClassification,
            TimesformerModel,
            TimesformerPreTrainedModel,
        )

else:
    import sys

    __UpperCamelCase    :						List[Any]     =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



