


'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase      :		Any			=      logging.get_logger(__name__)

__lowercase      :		int			=      {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}





class 				__UpperCamelCase							(						_snake_case   ):
	A_           =       "cvt"




	def __init__(  self		,							__a=3		,							__a=[7, 3, 3]		,							__a=[4, 2, 2]		,							__a=[2, 1, 1]		,							__a=[64, 192, 384]		,							__a=[1, 3, 6]		,							__a=[1, 2, 10]		,							__a=[4.0, 4.0, 4.0]		,							__a=[0.0, 0.0, 0.0]		,							__a=[0.0, 0.0, 0.0]		,							__a=[0.0, 0.0, 0.1]		,							__a=[True, True, True]		,							__a=[False, False, True]		,							__a=["dw_bn", "dw_bn", "dw_bn"]		,							__a=[3, 3, 3]		,							__a=[1, 1, 1]		,							__a=[2, 2, 2]		,							__a=[1, 1, 1]		,							__a=[1, 1, 1]		,							__a=0.02		,							__a=1E-1_2		,							**__a		,							):

						'''simple docstring'''





						super().__init__(**__lowerCamelCase				)
						__a     :			List[str]          =    num_channels
						__a     :			Dict          =    patch_sizes
						__a     :			str          =    patch_stride
						__a     :			Any          =    patch_padding
						__a     :			List[str]          =    embed_dim
						__a     :			Optional[Any]          =    num_heads
						__a     :			List[str]          =    depth
						__a     :			Any          =    mlp_ratio
						__a     :			Any          =    attention_drop_rate
						__a     :			Optional[int]          =    drop_rate
						__a     :			Any          =    drop_path_rate
						__a     :			Dict          =    qkv_bias
						__a     :			List[str]          =    cls_token
						__a     :			str          =    qkv_projection_method
						__a     :			List[Any]          =    kernel_qkv
						__a     :			List[str]          =    padding_kv
						__a     :			str          =    stride_kv
						__a     :			int          =    padding_q
						__a     :			Optional[int]          =    stride_q
						__a     :			Optional[Any]          =    initializer_range
						__a     :			str          =    layer_norm_eps







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''decoder.output_projection.weight''',
       '''_float_tensor''',
       '''encoder.embed_positions._float_tensor''',
       '''decoder.embed_positions._float_tensor''',
   ]
   for k in ignore_keys:
      state_dict.pop(UpperCamelCase_,   UpperCamelCase_						)


def        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   max_position_embeddings=1_024,   encoder_layers=args.encoder_layers,   decoder_layers=args.decoder_layers,   encoder_attention_heads=args.encoder_attention_heads,   decoder_attention_heads=args.decoder_attention_heads,   encoder_ffn_dim=args.encoder_ffn_embed_dim,   decoder_ffn_dim=args.decoder_ffn_embed_dim,   d_model=args.encoder_embed_dim,   encoder_layerdrop=args.encoder_layerdrop,   decoder_layerdrop=args.decoder_layerdrop,   dropout=args.dropout,   attention_dropout=args.attention_dropout,   activation_dropout=args.activation_dropout,   activation_function='''relu''',   )

   UpperCAmelCase__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


if __name__ == "__main__":
    __lowerCamelCase												=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument('''fairseq_path''', type=str, help='''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.''')
    __lowerCamelCase												=						parser.parse_args()
    __lowerCamelCase												=						convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)



