



'''simple docstring'''





import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__  :			Optional[Any]          =					logging.get_logger(__name__)

UpperCamelCase__  :			Optional[Any]          =					{
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}


class 				_lowerCAmelCase     (   __lowerCamelCase				):


 """simple docstring"""

 lowerCamelCase		       =			"segformer"







 def __init__(	self ,  _lowerCamelCase=3 ,  _lowerCamelCase=4 ,  _lowerCamelCase=[2, 2, 2, 2] ,  _lowerCamelCase=[8, 4, 2, 1] ,  _lowerCamelCase=[32, 64, 160, 256] ,  _lowerCamelCase=[7, 3, 3, 3] ,  _lowerCamelCase=[4, 2, 2, 2] ,  _lowerCamelCase=[1, 2, 5, 8] ,  _lowerCamelCase=[4, 4, 4, 4] ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.0 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=1e-6 ,  _lowerCamelCase=256 ,  _lowerCamelCase=255 ,  **_lowerCamelCase ,  )  ->  Tuple:
    super().__init__(**__A   )

    if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
       warnings.warn(
           """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
           """ removed, as the behaviour will default to that of reshape_last_stage = True.""" ,  __A ,  )

    A_   :						Optional[Any]		= num_channels
    A_   :						Tuple		= num_encoder_blocks
    A_   :						str		= depths
    A_   :						List[Any]		= sr_ratios
    A_   :						List[Any]		= hidden_sizes
    A_   :						Optional[Any]		= patch_sizes
    A_   :						Optional[Any]		= strides
    A_   :						List[str]		= mlp_ratios
    A_   :						str		= num_attention_heads
    A_   :						Optional[int]		= hidden_act
    A_   :						List[Any]		= hidden_dropout_prob
    A_   :						Any		= attention_probs_dropout_prob
    A_   :						str		= classifier_dropout_prob
    A_   :						str		= initializer_range
    A_   :						str		= drop_path_rate
    A_   :						List[str]		= layer_norm_eps
    A_   :						Any		= decoder_hidden_size
    A_   :						List[Any]		= kwargs.get("""reshape_last_stage""" ,  __A   )
    A_   :						int		= semantic_loss_ignore_index


class 				_lowerCAmelCase     (   __lowerCamelCase				):


 """simple docstring"""

 lowerCamelCase		       =			version.parse('''1.11'''				)







 @property
 def 	UpperCAmelCase_    (	self   )  ->  Optional[int]:
    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]   )







 @property
 def 	UpperCAmelCase_    (	self   )  ->  int:
    return 1e-4







 @property
 def 	UpperCAmelCase_    (	self   )  ->  Dict:
    return 12







'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  [
           '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 				lowercase__   (   __lowercase						:  Tuple     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       ,						__UpperCamelCase       =  emb.weight.shape
       __UpperCamelCase       =  nn.Linear(__lowercase				,  __lowercase				,  bias=__lowercase     )
       __UpperCamelCase       =  emb.weight.data
       return lin_layer
def 				lowercase__   (   __lowercase						:  int				,  __lowercase						:  List[str]="facebook/mbart-large-en-ro"				,  __lowercase						:  str=False				,  __lowercase						:  List[Any]=False     ) ->      int:



       """simple docstring"""






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

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

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

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

       return model


if __name__ == "__main__":
    a__							:							Dict =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''')
    a__							:							Union[str, Any] =parser.parse_args()
    a__							:							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)
