
"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def       _snake_case				(		snake_case__   :     int     ):
 A						   =   [
     '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       _snake_case				(		snake_case__   :     Any     ):
 A   ,      A						   =   emb.weight.shape
 A						   =   nn.Linear(snake_case__	,      snake_case__	,      bias=snake_case__     )
 A						   =   emb.weight.data
 return lin_layer






def       _snake_case				(		snake_case__   :     Optional[Any]	,      snake_case__   :     int="facebook/mbart-large-en-ro"	,      snake_case__   :     Any=False	,      snake_case__   :     int=False     ):
 A						   =   torch.load(snake_case__	,      map_location='cpu'     )['model']
 remove_ignore_keys_(snake_case__     )
 A						   =   state_dict['encoder.embed_tokens.weight'].shape[0]

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

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

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

 return model


if __name__ == "__main__":
  _lowercase							   =				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							   =				parser.parse_args()
  _lowercase							   =				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_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase							   =				{
    '''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PerceiverConfig''', '''PerceiverOnnxConfig'''],
    '''tokenization_perceiver''': ['''PerceiverTokenizer'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				['''PerceiverFeatureExtractor''']
		_lowercase							   =				['''PerceiverImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase							   =				[
		    '''PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PerceiverForImageClassificationConvProcessing''',
		    '''PerceiverForImageClassificationFourier''',
		    '''PerceiverForImageClassificationLearned''',
		    '''PerceiverForMaskedLM''',
		    '''PerceiverForMultimodalAutoencoding''',
		    '''PerceiverForOpticalFlow''',
		    '''PerceiverForSequenceClassification''',
		    '''PerceiverLayer''',
		    '''PerceiverModel''',
		    '''PerceiverPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

		_lowercase							   =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)