

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_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(_A   ,	_A       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					emb.weight.shape
     SCREAMING_SNAKE_CASE__					=					nn.Linear(_A   ,	_A   ,	bias=_A       )
     SCREAMING_SNAKE_CASE__					=					emb.weight.data
     return lin_layer
def        UpperCAmelCase_     (		_A   ,	_A="facebook/mbart-large-en-ro"   ,	_A=False   ,	_A=False       ):





     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE__					=					MBartConfig.from_pretrained(_A   ,	vocab_size=_A       )
     if mbart_aa and finetuned:
          SCREAMING_SNAKE_CASE__					=					'''relu'''

     SCREAMING_SNAKE_CASE__					=					state_dict['''decoder.embed_tokens.weight''']
     SCREAMING_SNAKE_CASE__					=					MBartForConditionalGeneration(_A       )
     model.model.load_state_dict(_A       )

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

     return model


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Union[str, 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''')
       _SCREAMING_SNAKE_CASE  :						int         =    parser.parse_args()
       _SCREAMING_SNAKE_CASE  :						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)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE  :						Dict         =    {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						Tuple         =    ['''LongformerTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''LongformerForMaskedLM''',
           '''LongformerForMultipleChoice''',
           '''LongformerForQuestionAnswering''',
           '''LongformerForSequenceClassification''',
           '''LongformerForTokenClassification''',
           '''LongformerModel''',
           '''LongformerPreTrainedModel''',
           '''LongformerSelfAttention''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE  :						List[Any]         =    [
           '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFLongformerForMaskedLM''',
           '''TFLongformerForMultipleChoice''',
           '''TFLongformerForQuestionAnswering''',
           '''TFLongformerForSequenceClassification''',
           '''TFLongformerForTokenClassification''',
           '''TFLongformerModel''',
           '''TFLongformerPreTrainedModel''',
           '''TFLongformerSelfAttention''',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

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


