


'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__snake_case    						={
    """configuration_efficientnet""": [
        """EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientNetConfig""",
        """EfficientNetOnnxConfig""",
    ]
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case    						=["""EfficientNetImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case    						=[
           """EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """EfficientNetForImageClassification""",
           """EfficientNetModel""",
           """EfficientNetPreTrainedModel""",
       ]

if TYPE_CHECKING:
       from .configuration_efficientnet import (
           EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
           EfficientNetConfig,
           EfficientNetOnnxConfig,
       )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_efficientnet import EfficientNetImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_efficientnet import (
                  EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  EfficientNetForImageClassification,
                  EfficientNetModel,
                  EfficientNetPreTrainedModel,
              )

else:
       import sys

       __snake_case    						=_LazyModule(__name__, globals()["""__file__"""], _import_structure)









'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

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

       return model


if __name__ == "__main__":
 lowerCamelCase							:   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')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	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)




