
from typing import TYPE_CHECKING

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


_lowerCamelCase	:    Optional[int]          =      {
    '''configuration_audio_spectrogram_transformer''': [
        '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ASTConfig''',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase	:    Union[str, Any]          =      [
				    '''AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''ASTForAudioClassification''',
				    '''ASTModel''',
				    '''ASTPreTrainedModel''',
				]

try:
				if not is_speech_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCamelCase	:    Dict          =      ['''ASTFeatureExtractor''']

if TYPE_CHECKING:
				from .configuration_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    ASTConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_audio_spectrogram_transformer import (
								    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ASTForAudioClassification,
								    ASTModel,
								    ASTPreTrainedModel,
								)

				try:
								if not is_speech_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
				import sys

				_lowerCamelCase	:    str          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 	a_		(	__lowercase       :     Dict			)			->  int:
			_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(__lowercase	, __lowercase			)






def 	a_		(	__lowercase       :     List[str]			)			->  str:
			_snake_case     , _snake_case          =      emb.weight.shape
			_snake_case          =      nn.Linear(__lowercase	, __lowercase	, bias=__lowercase			)
			_snake_case          =      emb.weight.data
			return lin_layer






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

			_snake_case          =      MBartConfig.from_pretrained(__lowercase	, vocab_size=__lowercase			)
			if mbart_aa and finetuned:
						_snake_case          =      'relu'

			_snake_case          =      state_dict['decoder.embed_tokens.weight']
			_snake_case          =      MBartForConditionalGeneration(__lowercase			)
			model.model.load_state_dict(__lowercase			)

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

			return model


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